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Description:

igel 1.0.0

A delightful machine learning tool that allows you to train/fit, test and use models without writing code

Note
I’m also working on a GUI desktop app for igel based on people’s requests. You can find it under
Igel-UI.

Free software: MIT license
Documentation: https://igel.readthedocs.io.

Table of Contents

Introduction
Features
Installation
Models
Quick Start
Usage

Configuration Step
Training
Evaluation
Prediction
Experiment
Use igel from python (instead of terminal)
Serve the model
Use igel from python (instead of terminal)

Overview
Read Data Options
E2E Example
Advanced Usage
Examples
Auto ML Examples

ImageClassification
TextClassification

GUI
Running with Docker
Links
Help/GetHelp
Contributions
License

Introduction
The goal of the project is to provide machine learning for everyone, both technical and non-technical
users.
I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build
some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck at writing
boilerplate code and thinking too much where to start. Therefore, I decided to create this tool.
igel is built on top of other ML frameworks. It provides a simple way to use machine learning without writing
a single line of code. Igel is highly customizable, but only if you want to. Igel does not force you to
customize anything. Besides default values, igel can use auto-ml features to figure out a model that can work great with your data.
All you need is a yaml (or json) file, where you need to describe what you are trying to do. That’s it!
Igel supports regression, classification and clustering.
Igel’s supports auto-ml features like ImageClassification and TextClassification
Igel supports most used dataset types in the data science field. For instance, your input dataset can be
a csv, txt, excel sheet, json or even html file that you want to fetch. If you are using auto-ml features, then you can even
feed raw data to igel and it will figure out how to deal with it. More on this later in the examples.

Features

Supports most dataset types (csv, txt, excel, json, html) even just raw data stored in folders
Supports all state of the art machine learning models (even preview models)
Supports different data preprocessing methods
Provides flexibility and data control while writing configurations
Supports cross validation
Supports both hyperparameter search (version >= 0.2.8)
Supports yaml and json format
Usage from GUI
Supports different sklearn metrics for regression, classification and clustering
Supports multi-output/multi-target regression and classification
Supports multi-processing for parallel model construction
Support for auto machine learning

Installation

The easiest way is to install igel using pip

$ pip install -U igel

Models
Igel’s supported models:
+--------------------+----------------------------+-------------------------+
| regression | classification | clustering |
+--------------------+----------------------------+-------------------------+
| LinearRegression | LogisticRegression | KMeans |
| Lasso | Ridge | AffinityPropagation |
| LassoLars | DecisionTree | Birch |
| BayesianRegression | ExtraTree | AgglomerativeClustering |
| HuberRegression | RandomForest | FeatureAgglomeration |
| Ridge | ExtraTrees | DBSCAN |
| PoissonRegression | SVM | MiniBatchKMeans |
| ARDRegression | LinearSVM | SpectralBiclustering |
| TweedieRegression | NuSVM | SpectralCoclustering |
| TheilSenRegression | NearestNeighbor | SpectralClustering |
| GammaRegression | NeuralNetwork | MeanShift |
| RANSACRegression | PassiveAgressiveClassifier | OPTICS |
| DecisionTree | Perceptron | KMedoids |
| ExtraTree | BernoulliRBM | ---- |
| RandomForest | BoltzmannMachine | ---- |
| ExtraTrees | CalibratedClassifier | ---- |
| SVM | Adaboost | ---- |
| LinearSVM | Bagging | ---- |
| NuSVM | GradientBoosting | ---- |
| NearestNeighbor | BernoulliNaiveBayes | ---- |
| NeuralNetwork | CategoricalNaiveBayes | ---- |
| ElasticNet | ComplementNaiveBayes | ---- |
| BernoulliRBM | GaussianNaiveBayes | ---- |
| BoltzmannMachine | MultinomialNaiveBayes | ---- |
| Adaboost | ---- | ---- |
| Bagging | ---- | ---- |
| GradientBoosting | ---- | ---- |
+--------------------+----------------------------+-------------------------+
For auto ML:

ImageClassifier
TextClassifier
ImageRegressor
TextRegressor
StructeredDataClassifier
StructeredDataRegressor
AutoModel

Quick Start
The help command is very useful to check supported commands and corresponding args/options
$ igel --help
You can also run help on sub-commands, for example:
$ igel fit --help
Igel is highly customizable. If you know what you want and want to configure your model manually,
then check the next sections, which will guide you on how to write a yaml or a json config file.
After that, you just have to tell igel, what to do and where to find your data and config file.
Here is an example:
$ igel fit --data_path 'path_to_your_csv_dataset.csv' --yaml_path 'path_to_your_yaml_file.yaml'
However, you can also use the auto-ml features and let igel do everything for you.
A great example for this would be image classification. Let’s imagine you already have a dataset
of raw images stored in a folder called images
All you have to do is run:
$ igel auto-train --data_path 'path_to_your_images_folder' --task ImageClassification
That’s it! Igel will read the images from the directory,
process the dataset (converting to matrices, rescale, split, etc…) and start training/optimizing
a model that works good on your data. As you can see it’s pretty easy, you just have to provide the path
to your data and the task you want to perform.

Note
This feature is computationally expensive as igel would try many
different models and compare their performance in order to find the ‘best’ one.

Usage
You can run the help command to get instructions. You can also run help on sub-commands!
$ igel --help

Configuration Step
First step is to provide a yaml file (you can also use json if you want)
You can do this manually by creating a .yaml file (called igel.yaml by convention but you can name if whatever you want)
and editing it yourself.
However, if you are lazy (and you probably are, like me :D), you can use the igel init command to get started fast,
which will create a basic config file for you on the fly.
"""
igel init --help

Example:
If I want to use neural networks to classify whether someone is sick or not using the indian-diabetes dataset,
then I would use this command to initialize a yaml file n.b. you may need to rename outcome column in .csv to sick:

$ igel init -type "classification" -model "NeuralNetwork" -target "sick"
"""
$ igel init
After running the command, an igel.yaml file will be created for you in the current working directory. You can
check it out and modify it if you want to, otherwise you can also create everything from scratch.

Demo:

# model definition
model:
# in the type field, you can write the type of problem you want to solve. Whether regression, classification or clustering
# Then, provide the algorithm you want to use on the data. Here I'm using the random forest algorithm
type: classification
algorithm: RandomForest # make sure you write the name of the algorithm in pascal case
arguments:
n_estimators: 100 # here, I set the number of estimators (or trees) to 100
max_depth: 30 # set the max_depth of the tree

# target you want to predict
# Here, as an example, I'm using the famous indians-diabetes dataset, where I want to predict whether someone have diabetes or not.
# Depending on your data, you need to provide the target(s) you want to predict here
target:
- sick
In the example above, I’m using random forest to classify whether someone have
diabetes or not depending on some features in the dataset
I used the famous indian diabetes in this example indian-diabetes dataset)
Notice that I passed n_estimators and max_depth as additional arguments to the model.
If you don’t provide arguments then the default will be used.
You don’t have to memorize the arguments for each model. You can always run igel models in your terminal, which will
get you to interactive mode, where you will be prompted to enter the model you want to use and type of the problem
you want to solve. Igel will then show you information about the model and a link that you can follow to see
a list of available arguments and how to use these.

Training

The expected way to use igel is from terminal (igel CLI):

Run this command in terminal to fit/train a model, where you provide the path to your dataset and the path to the yaml file
$ igel fit --data_path 'path_to_your_csv_dataset.csv' --yaml_path 'path_to_your_yaml_file.yaml'

# or shorter

$ igel fit -dp 'path_to_your_csv_dataset.csv' -yml 'path_to_your_yaml_file.yaml'

"""
That's it. Your "trained" model can be now found in the model_results folder
(automatically created for you in your current working directory).
Furthermore, a description can be found in the description.json file inside the model_results folder.
"""

Demo:

Evaluation
You can then evaluate the trained/pre-fitted model:
$ igel evaluate -dp 'path_to_your_evaluation_dataset.csv'
"""
This will automatically generate an evaluation.json file in the current directory, where all evaluation results are stored
"""

Demo:

Prediction
Finally, you can use the trained/pre-fitted model to make predictions if you are happy with the evaluation results:
$ igel predict -dp 'path_to_your_test_dataset.csv'
"""
This will generate a predictions.csv file in your current directory, where all predictions are stored in a csv file
"""

Demo:

Experiment
You can combine the train, evaluate and predict phases using one single command called experiment:
$ igel experiment -DP "path_to_train_data path_to_eval_data path_to_test_data" -yml "path_to_yaml_file"

"""
This will run fit using train_data, evaluate using eval_data and further generate predictions using the test_data
"""

Demo:

Use igel from python (instead of terminal)

Alternatively, you can also write code if you want to:

from igel import Igel

Igel(cmd="fit", data_path="path_to_your_dataset", yaml_path="path_to_your_yaml_file")
"""
check the examples folder for more
"""

Serve the model
The next step is to use your model in production. Igel helps you with this task too by providing the serve command.
Running the serve command will tell igel to serve your model. Precisely, igel will automatically build
a REST server and serve your model on a specific host and port, which you can configure by passing these as
cli options.
The easiest way is to run:
$ igel serve --model_results_dir "path_to_model_results_directory"
Notice that igel needs the –model_results_dir or shortly -res_dir cli option in order to load the model and start the server.
By default, igel will serve your model on localhost:8000, however, you can easily override this by providing a host
and a port cli options.
$ igel serve --model_results_dir "path_to_model_results_directory" --host "127.0.0.1" --port 8000
Igel uses FastAPI for creating the REST server, which is a modern high performance
framework
and uvicorn to run it under the hood.

Use igel from python (instead of terminal)

Alternatively, you can also write code if you want to:

from igel import Igel

Igel(cmd="fit", data_path="path_to_your_dataset", yaml_path="path_to_your_yaml_file")
"""
check the examples folder for more
"""

Overview
The main goal of igel is to provide you with a way to train/fit, evaluate and use models without writing code.
Instead, all you need is to provide/describe what you want to do in a simple yaml file.
Basically, you provide description or rather configurations in the yaml file as key value pairs.
Here is an overview of all supported configurations (for now):
# dataset operations
dataset:
type: csv # [str] -> type of your dataset
read_data_options: # options you want to supply for reading your data (See the detailed overview about this in the next section)
sep: # [str] -> Delimiter to use.
delimiter: # [str] -> Alias for sep.
header: # [int, list of int] -> Row number(s) to use as the column names, and the start of the data.
names: # [list] -> List of column names to use
index_col: # [int, str, list of int, list of str, False] -> Column(s) to use as the row labels of the DataFrame,
usecols: # [list, callable] -> Return a subset of the columns
squeeze: # [bool] -> If the parsed data only contains one column then return a Series.
prefix: # [str] -> Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …
mangle_dupe_cols: # [bool] -> Duplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.
dtype: # [Type name, dict maping column name to type] -> Data type for data or columns
engine: # [str] -> Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.
converters: # [dict] -> Dict of functions for converting values in certain columns. Keys can either be integers or column labels.
true_values: # [list] -> Values to consider as True.
false_values: # [list] -> Values to consider as False.
skipinitialspace: # [bool] -> Skip spaces after delimiter.
skiprows: # [list-like] -> Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.
skipfooter: # [int] -> Number of lines at bottom of file to skip
nrows: # [int] -> Number of rows of file to read. Useful for reading pieces of large files.
na_values: # [scalar, str, list, dict] -> Additional strings to recognize as NA/NaN.
keep_default_na: # [bool] -> Whether or not to include the default NaN values when parsing the data.
na_filter: # [bool] -> Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.
verbose: # [bool] -> Indicate number of NA values placed in non-numeric columns.
skip_blank_lines: # [bool] -> If True, skip over blank lines rather than interpreting as NaN values.
parse_dates: # [bool, list of int, list of str, list of lists, dict] -> try parsing the dates
infer_datetime_format: # [bool] -> If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them.
keep_date_col: # [bool] -> If True and parse_dates specifies combining multiple columns then keep the original columns.
dayfirst: # [bool] -> DD/MM format dates, international and European format.
cache_dates: # [bool] -> If True, use a cache of unique, converted dates to apply the datetime conversion.
thousands: # [str] -> the thousands operator
decimal: # [str] -> Character to recognize as decimal point (e.g. use ‘,’ for European data).
lineterminator: # [str] -> Character to break file into lines.
escapechar: # [str] -> One-character string used to escape other characters.
comment: # [str] -> Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character.
encoding: # [str] -> Encoding to use for UTF when reading/writing (ex. ‘utf-8’).
dialect: # [str, csv.Dialect] -> If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting
delim_whitespace: # [bool] -> Specifies whether or not whitespace (e.g. ' ' or ' ') will be used as the sep
low_memory: # [bool] -> Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference.
memory_map: # [bool] -> If a filepath is provided for filepath_or_buffer, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.

random_numbers: # random numbers options in case you wanted to generate the same random numbers on each run
generate_reproducible: # [bool] -> set this to true to generate reproducible results
seed: # [int] -> the seed number is optional. A seed will be set up for you if you didn't provide any

split: # split options
test_size: 0.2 #[float] -> 0.2 means 20% for the test data, so 80% are automatically for training
shuffle: true # [bool] -> whether to shuffle the data before/while splitting
stratify: None # [list, None] -> If not None, data is split in a stratified fashion, using this as the class labels.

preprocess: # preprocessing options
missing_values: mean # [str] -> other possible values: [drop, median, most_frequent, constant] check the docs for more
encoding:
type: oneHotEncoding # [str] -> other possible values: [labelEncoding]
scale: # scaling options
method: standard # [str] -> standardization will scale values to have a 0 mean and 1 standard deviation | you can also try minmax
target: inputs # [str] -> scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled

# model definition
model:
type: classification # [str] -> type of the problem you want to solve. | possible values: [regression, classification, clustering]
algorithm: NeuralNetwork # [str (notice the pascal case)] -> which algorithm you want to use. | type igel algorithms in the Terminal to know more
arguments: # model arguments: you can check the available arguments for each model by running igel help in your terminal
use_cv_estimator: false # [bool] -> if this is true, the CV class of the specific model will be used if it is supported
cross_validate:
cv: # [int] -> number of kfold (default 5)
n_jobs: # [signed int] -> The number of CPUs to use to do the computation (default None)
verbose: # [int] -> The verbosity level. (default 0)
hyperparameter_search:
method: grid_search # method you want to use: grid_search and random_search are supported
parameter_grid: # put your parameters grid here that you want to use, an example is provided below
param1: [val1, val2]
param2: [val1, val2]
arguments: # additional arguments you want to provide for the hyperparameter search
cv: 5 # number of folds
refit: true # whether to refit the model after the search
return_train_score: false # whether to return the train score
verbose: 0 # verbosity level

# target you want to predict
target: # list of strings: basically put here the column(s), you want to predict that exist in your csv dataset
- put the target you want to predict here
- you can assign many target if you are making a multioutput prediction

Read Data Options

Note
igel uses pandas under the hood to read & parse the data. Hence, you can
find this data optional parameters also in the pandas official documentation.

A detailed overview of the configurations you can provide in the yaml (or json) file is given below.
Notice that you will certainly not need all the configuration values for the dataset. They are optional.
Generally, igel will figure out how to read your dataset.
However, you can help it by providing extra fields using this read_data_options section.
For example, one of the helpful values in my opinion is the “sep”, which defines how your columns
in the csv dataset are separated. Generally, csv datasets are separated by commas, which is also the default value
here. However, it may be separated by a semicolon in your case.
Hence, you can provide this in the read_data_options. Just add the sep: ";" under read_data_options.

Supported Read Data Options

Parameter
Type
Explanation

sep
str, default ‘,’
Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python’s builtin sniffer tool, csv.Sniffer. In addition, separators longer than 1 character and different from ‘s+’ will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: ‘rt’.

delimiter
default None
Alias for sep.

header
int, list of int, default ‘infer’
Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

names
array-like, optional
List of column names to use. If the file contains a header row, then you should explicitly pass header=0 to override the column names. Duplicates in this list are not allowed.

index_col
int, str, sequence of int / str, or False, default None
Column(s) to use as the row labels of the DataFrame, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used. Note: index_col=False can be used to force pandas to not use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line.

usecols
list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid list-like usecols parameter would be [0, 1, 2] or [‘foo’, ‘bar’, ‘baz’]. Element order is ignored, so usecols=[0, 1] is the same as [1, 0]. To instantiate a DataFrame from data with element order preserved use pd.read_csv(data, usecols=[‘foo’, ‘bar’])[[‘foo’, ‘bar’]] for columns in [‘foo’, ‘bar’] order or pd.read_csv(data, usecols=[‘foo’, ‘bar’])[[‘bar’, ‘foo’]] for [‘bar’, ‘foo’] order. If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be lambda x: x.upper() in [‘AAA’, ‘BBB’, ‘DDD’]. Using this parameter results in much faster parsing time and lower memory usage.

squeeze
bool, default False
If the parsed data only contains one column then return a Series.

prefix
str, optional
Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …

mangle_dupe_cols
bool, default True
Duplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

dtype
{‘c’, ‘python’}, optional
Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

converters
dict, optional
Dict of functions for converting values in certain columns. Keys can either be integers or column labels.

true_values
list, optional
Values to consider as True.

false_values
list, optional
Values to consider as False.

skipinitialspace
bool, default False
Skip spaces after delimiter.

skiprows
list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be lambda x: x in [0, 2].

skipfooter
int, default 0
Number of lines at bottom of file to skip (Unsupported with engine=’c’).

nrows
int, optional
Number of rows of file to read. Useful for reading pieces of large files.

na_values
scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.

keep_default_na
bool, default True
Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows: If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing. If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing. If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing. If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN. Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.

na_filter
bool, default True
Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.

verbose
bool, default False
Indicate number of NA values placed in non-numeric columns.

skip_blank_lines
bool, default True
If True, skip over blank lines rather than interpreting as NaN values.

parse_dates
bool or list of int or names or list of lists or dict, default False
The behavior is as follows: boolean. If True -> try parsing the index. list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’ If a column or index cannot be represented as an array of datetimes, say because of an unparseable value or a mixture of timezones, the column or index will be returned unaltered as an object data type.

infer_datetime_format
bool, default False
If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x.

keep_date_col
bool, default False
If True and parse_dates specifies combining multiple columns then keep the original columns.

date_parser
function, optional
Function to use for converting a sequence of string columns to an array of datetime instances. The default uses dateutil.parser.parser to do the conversion. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one or more strings (corresponding to the columns defined by parse_dates) as arguments.

dayfirst
bool, default False
DD/MM format dates, international and European format.

cache_dates
bool, default True
If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets.

thousands
str, optional
Thousands separator.

decimal
str, default ‘.’
Character to recognize as decimal point (e.g. use ‘,’ for European data).

lineterminator
str (length 1), optional
Character to break file into lines. Only valid with C parser.

escapechar
str (length 1), optional
One-character string used to escape other characters.

comment
str, optional
Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether.

encoding
str, optional
Encoding to use for UTF when reading/writing (ex. ‘utf-8’).

dialect
str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting

low_memory
bool, default True
Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the dtype parameter. Note that the entire file is read into a single DataFrame regardless,

memory_map
bool, default False
map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.

E2E Example
A complete end to end solution is provided in this section to prove the capabilities of igel.
As explained previously, you need to create a yaml configuration file. Here is an end to end example for
predicting whether someone have diabetes or not using the decision tree algorithm. The dataset can be found in the examples folder.

Fit/Train a model:

model:
type: classification
algorithm: DecisionTree

target:
- sick
$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file
That’s it, igel will now fit the model for you and save it in a model_results folder in your current directory.

Evaluate the model:

Evaluate the pre-fitted model. Igel will load the pre-fitted model from the model_results directory and evaluate it for you.
You just need to run the evaluate command and provide the path to your evaluation data.
$ igel evaluate -dp path_to_the_evaluation_dataset
That’s it! Igel will evaluate the model and store statistics/results in an evaluation.json file inside the model_results folder

Predict:

Use the pre-fitted model to predict on new data. This is done automatically by igel, you just need to provide the
path to your data that you want to use prediction on.
$ igel predict -dp path_to_the_new_dataset
That’s it! Igel will use the pre-fitted model to make predictions and save it in a predictions.csv file inside the model_results folder

Advanced Usage
You can also carry out some preprocessing methods or other operations by providing them in the yaml file.
Here is an example, where the data is split to 80% for training and 20% for validation/testing.
Also, the data are shuffled while splitting.
Furthermore, the data are preprocessed by replacing missing values with the mean ( you can also use median, mode etc..).
check this link for more information
# dataset operations
dataset:
split:
test_size: 0.2
shuffle: True
stratify: default

preprocess: # preprocessing options
missing_values: mean # other possible values: [drop, median, most_frequent, constant] check the docs for more
encoding:
type: oneHotEncoding # other possible values: [labelEncoding]
scale: # scaling options
method: standard # standardization will scale values to have a 0 mean and 1 standard deviation | you can also try minmax
target: inputs # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled

# model definition
model:
type: classification
algorithm: RandomForest
arguments:
# notice that this is the available args for the random forest model. check different available args for all supported models by running igel help
n_estimators: 100
max_depth: 20

# target you want to predict
target:
- sick
Then, you can fit the model by running the igel command as shown in the other examples
$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file
For evaluation
$ igel evaluate -dp path_to_the_evaluation_dataset
For production
$ igel predict -dp path_to_the_new_dataset

Examples
In the examples folder in the repository, you will find a data folder,where the famous indian-diabetes, iris dataset
and the linnerud (from sklearn) datasets are stored.
Furthermore, there are end to end examples inside each folder, where there are scripts and yaml files that
will help you get started.
The indian-diabetes-example folder contains two examples to help you get started:

The first example is using a neural network, where the configurations are stored in the neural-network.yaml file
The second example is using a random forest, where the configurations are stored in the random-forest.yaml file

The iris-example folder contains a logistic regression example, where some preprocessing (one hot encoding)
is conducted on the target column to show you more the capabilities of igel.
Furthermore, the multioutput-example contains a multioutput regression example.
Finally, the cv-example contains an example using the Ridge classifier using cross validation.
You can also find a cross validation and a hyperparameter search examples in the folder.
I suggest you play around with the examples and igel cli. However,
you can also directly execute the fit.py, evaluate.py and predict.py if you want to.

Auto ML Examples

ImageClassification
First, create or modify a dataset of images that are categorized into sub-folders based on the image label/class
For example, if you are have dogs and cats images, then you will need 2 sub-folders:

folder 0, which contains cats images (here the label 0 indicates a cat)
folder 1, which contains dogs images (here the label 1 indicates a dog)

Assuming these two sub-folder are contained in one parent folder called images, just feed data to igel:
$ igel auto-train -dp ./images --task ImageClassification
Igel will handle everything from pre-processing the data to optimizing hyperparameters. At the end,
the best model will be stored in the current working dir.

TextClassification
First, create or modify a text dataset that are categorized into sub-folders based on the text label/class
For example, if you are have a text dataset of positive and negative feedbacks, then you will need 2 sub-folders:

folder 0, which contains negative feedbacks (here the label 0 indicates a negative one)
folder 1, which contains positive feedbacks (here the label 1 indicates a positive one)

Assuming these two sub-folder are contained in one parent folder called texts, just feed data to igel:
$ igel auto-train -dp ./texts --task TextClassification
Igel will handle everything from pre-processing the data to optimizing hyperparameters. At the end,
the best model will be stored in the current working dir.

GUI
You can also run the igel UI if you are not familiar with the terminal. Just install igel on your machine
as mentioned above. Then run this single command in your terminal
$ igel gui
This will open up the gui, which is very simple to use. Check examples of how the gui looks like and how to use it
here: https://github.com/nidhaloff/igel-ui

Running with Docker

Use the official image (recommended):

You can pull the image first from docker hub
$ docker pull nidhaloff/igel
Then use it:
$ docker run -it --rm -v $(pwd):/data nidhaloff/igel fit -yml 'your_file.yaml' -dp 'your_dataset.csv'

Alternatively, you can create your own image locally if you want:

You can run igel inside of docker by first building the image:
$ docker build -t igel .
And then running it and attaching your current directory (does not need to be the igel directory) as /data (the workdir) inside of the container:
$ docker run -it --rm -v $(pwd):/data igel fit -yml 'your_file.yaml' -dp 'your_dataset.csv'

Links

Article: https://medium.com/@nidhalbacc/machine-learning-without-writing-code-984b238dd890

Help/GetHelp
If you are facing any problems, please feel free to open an issue.
Additionally, you can make contact with the author for further information/questions.
Do you like igel?
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Following on github and/or twitter
Star the github repo
Watch the github repo for new releases
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Create issues and pull requests
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Contributions
You think this project is useful and you want to bring new ideas, new features, bug fixes, extend the docs?
Contributions are always welcome.
Make sure you read the guidelines first

License
MIT license
Copyright (c) 2020-present, Nidhal Baccouri