AutoMLで手軽に予測モデルを作ってみたい！！！ By AutoGluon

!python -m pip install --upgrade pip
!python -m pip install autogluon

from autogluon.tabular import TabularDataset, TabularPredictor

data_url = 'https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/'
train_data = TabularDataset(f'{data_url}train.csv')
train_data.head()

label = 'signature'
train_data[label].describe()

count    10000.000000
mean        -0.022000
std          3.025166
min        -12.000000
25%         -2.000000
50%          0.000000
75%          2.000000
max         12.000000
Name: signature, dtype: float64

predictor = TabularPredictor(label=label).fit(train_data)

  No path specified. Models will be saved in: "AutogluonModels/ag-20241205_012036"
Verbosity: 2 (Standard Logging)
=================== System Info ===================
AutoGluon Version:  1.2
Python Version:     3.10.12
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
CPU Count:          2
Memory Avail:       11.57 GB / 12.67 GB (91.3%)
Disk Space Avail:   74.20 GB / 107.72 GB (68.9%)
===================================================
No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...
    Recommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):
    presets='experimental' : New in v1.2: Pre-trained foundation model + parallel fits. The absolute best accuracy without consideration for inference speed. Does not support GPU.
    presets='best'         : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.
    presets='high'         : Strong accuracy with fast inference speed.
    presets='good'         : Good accuracy with very fast inference speed.
    presets='medium'       : Fast training time, ideal for initial prototyping.
Beginning AutoGluon training ...
AutoGluon will save models to "/content/AutogluonModels/ag-20241205_012036"
Train Data Rows:    10000
Train Data Columns: 18
Label Column:       signature
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
    First 10 (of 13) unique label values:  [-2, 0, 2, -8, 4, -4, -6, 8, 6, 10]
    If 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
Problem Type:       multiclass
Preprocessing data ...
Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 9 out of 13 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.
Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9984
Train Data Class Count: 9
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
    Available Memory:                    11841.83 MB
    Train Data (Original)  Memory Usage: 1.37 MB (0.0% of available memory)
    Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
    Stage 1 Generators:
        Fitting AsTypeFeatureGenerator...
            Note: Converting 5 features to boolean dtype as they only contain 2 unique values.
    Stage 2 Generators:
        Fitting FillNaFeatureGenerator...
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
    Useless Original Features (Count: 1): ['Symmetry_D8']
        These features carry no predictive signal and should be manually investigated.
        This is typically a feature which has the same value for all rows.
        These features do not need to be present at inference time.
    Types of features in original data (raw dtype, special dtypes):
        ('float', []) : 14 | ['chern_simons', 'cusp_volume', 'injectivity_radius', 'longitudinal_translation', 'meridinal_translation_imag', ...]
        ('int', [])   :  3 | ['Unnamed: 0', 'hyperbolic_adjoint_torsion_degree', 'hyperbolic_torsion_degree']
    Types of features in processed data (raw dtype, special dtypes):
        ('float', [])     : 9 | ['chern_simons', 'cusp_volume', 'injectivity_radius', 'longitudinal_translation', 'meridinal_translation_imag', ...]
        ('int', [])       : 3 | ['Unnamed: 0', 'hyperbolic_adjoint_torsion_degree', 'hyperbolic_torsion_degree']
        ('int', ['bool']) : 5 | ['Symmetry_0', 'Symmetry_D3', 'Symmetry_D4', 'Symmetry_D6', 'Symmetry_Z/2 + Z/2']
    0.3s = Fit runtime
    17 features in original data used to generate 17 features in processed data.
    Train Data (Processed) Memory Usage: 0.96 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.42s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
    To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.1, Train Rows: 8985, Val Rows: 999
User-specified model hyperparameters to be fit:
{
    'NN_TORCH': [{}],
    'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],
    'CAT': [{}],
    'XGB': [{}],
    'FASTAI': [{}],
    'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
    'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
    'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
Fitting 13 L1 models, fit_strategy="sequential" ...
Fitting model: KNeighborsUnif ...
    0.2232   = Validation score   (accuracy)
    9.92s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: KNeighborsDist ...
    0.2132   = Validation score   (accuracy)
    0.05s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: NeuralNetFastAI ...
    0.9409   = Validation score   (accuracy)
    16.79s   = Training   runtime
    0.04s    = Validation runtime
Fitting model: LightGBMXT ...
/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: 
Dask dataframe query planning is disabled because dask-expr is not installed.

You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.

  warnings.warn(msg, FutureWarning)
    0.9459   = Validation score   (accuracy)
    10.69s   = Training   runtime
    0.24s    = Validation runtime
Fitting model: LightGBM ...
    0.956    = Validation score   (accuracy)
    9.71s    = Training   runtime
    0.33s    = Validation runtime
Fitting model: RandomForestGini ...
    0.9449   = Validation score   (accuracy)
    8.86s    = Training   runtime
    0.12s    = Validation runtime
Fitting model: RandomForestEntr ...
    0.9499   = Validation score   (accuracy)
    10.04s   = Training   runtime
    0.11s    = Validation runtime
Fitting model: CatBoost ...
    0.956    = Validation score   (accuracy)
    73.03s   = Training   runtime
    0.01s    = Validation runtime
Fitting model: ExtraTreesGini ...
    0.9469   = Validation score   (accuracy)
    4.42s    = Training   runtime
    0.13s    = Validation runtime
Fitting model: ExtraTreesEntr ...
    0.9429   = Validation score   (accuracy)
    2.84s    = Training   runtime
    0.13s    = Validation runtime
Fitting model: XGBoost ...
    0.957    = Validation score   (accuracy)
    16.0s    = Training   runtime
    0.35s    = Validation runtime
Fitting model: NeuralNetTorch ...
    0.9419   = Validation score   (accuracy)
    79.07s   = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBMLarge ...
    0.9499   = Validation score   (accuracy)
    16.1s    = Training   runtime
    0.42s    = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    Ensemble Weights: {'RandomForestEntr': 0.25, 'ExtraTreesGini': 0.25, 'KNeighborsUnif': 0.167, 'NeuralNetFastAI': 0.167, 'XGBoost': 0.083, 'NeuralNetTorch': 0.083}
    0.965    = Validation score   (accuracy)
    0.25s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 264.38s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 1512.1 rows/s (999 batch size)
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("/content/AutogluonModels/ag-20241205_012036")

test_data = TabularDataset(f'{data_url}test.csv')
y_pred = predictor.predict(test_data.drop(columns=[label]))

predictor.evaluate(test_data, silent=True)

{'accuracy': 0.9478,
 'balanced_accuracy': 0.754478262473782,
 'mcc': 0.9360368834449522}

predictor.leaderboard(test_data)