一行代码不用写，就可以训练、测试、使用模型，这个star量1.5k的项目帮你做到

# dataset operationsdataset:    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.

    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 definitionmodel:    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 predicttarget:  # 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

$ igel --help
# or just
$ igel -h"""Take some time and read the output of help command. You ll save time later if you understand how to use igel."""

"""igel init possible optional args are: (notice that these args are optional, so you can also just run igel init if you want)-type: regression, classification or clustering-model: model you want to use-target: target you want to predict

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:$ igel init -type "classification" -model "NeuralNetwork" -target "sick""""$ igel init

# model definitionmodel:    # 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 heretarget:    - sick

$ igel fit --data_path 'path_to_your_csv_dataset.csv' --yaml_file '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."""

$ 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"""

$ 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"""

$ 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"""

igel fit

model:    type: classification    algorithm: DecisionTree
target:    - sick

$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file