pwml 1.0.1

Python Wrappers for Machine Learning

Pwml

Pwml stands for Python Wrappers for Machine Learning

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Requirements

• Python >= 3.8
• See setup.py for the full dependency list

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Installation

pip install Pwml

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Modules

classifiers - Hierarchical Classification

HierarchicalClassifierModel trains a tree of sklearn pipelines, one per node in the label hierarchy. Each node's classifier is selected and tuned independently via GridSearchCV. Inference cascades top-down through the tree.

Features:

• Text embedding via sentence-transformers (all-MiniLM-L6-v2 384-dim, all-mpnet-base-v2 768-dim)
• One-hot encoding for categorical features
• Numeric passthrough
• Platt calibration with per-class threshold optimization
• Soft routing: descent stops when prediction confidence falls below a configurable threshold
• Batch inference via predict_dataframe
• Evaluation and stratified cross-validation

Training

from pwml.classifiers import hierarchical as hc
from pwml.classifiers import features as fe

model = hc.HierarchicalClassifierModel(
    model_name='my_model',
    experiment_name='experiment_1',
    input_features=[
        fe.InputFeature(feature_name='Style',    feature_type='text'),
        fe.InputFeature(feature_name='Gender',   feature_type='text'),
        fe.InputFeature(feature_name='Brand',    feature_type='text'),
        fe.InputFeature(feature_name='Price',    feature_type='numeric'),
        fe.InputFeature(feature_name='Category', feature_type='category'),
    ],
    output_feature_hierarchy=fe.OutputFeature(
        feature_name='Division',
        child_feature=fe.OutputFeature(feature_name='Class')))

model.load_from_dataframe(data=df)
model.save_model(filepath='my_model.joblib')

The model trains n+1 classifiers where n is the number of distinct Division values: one classifier for the top-level Division prediction, and one per Division value for the Class prediction within that division.

Single-sample inference

model = hc.HierarchicalClassifierModel.load_model(filepath='my_model.joblib')

result = model.predict(
    input={'Style': 'slim fit jeans', 'Gender': 'men', 'Brand': 'Acme', 'Price': 49.99, 'Category': 'Bottoms'},
    min_routing_confidence=0.6)

# result is a list of dicts, one per hierarchy level:
# [{'feature_name': 'Division', 'value': 'Apparel', 'confidence': 0.91},
#  {'feature_name': 'Class',    'value': 'Denim',   'confidence': 0.78}]

Batch inference

predictions_df = model.predict_dataframe(data=df)
# Returns df with extra columns: Division_predicted, Division_confidence, Class_predicted, Class_confidence

Evaluation and cross-validation

metrics, predictions_df = model.evaluate(data=df)

summary, per_fold = model.cross_validate(data=df, n_splits=5)
print(summary)  # {'Division': {'mean': 0.87, 'std': 0.02}, 'Class': {'mean': 0.74, 'std': 0.04}}

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timeseries - Time Series Utilities

Data augmentation

from pwml.timeseries import dataaugmentationhelpers as dah

# Split data before calling prepare_data to avoid scaler leakage
train_df = df.iloc[:split]
test_df  = df.iloc[split:]

X_train, y_train, index, scaler_in, scaler_out, n_samples = dah.prepare_data(
    data=train_df,
    lags_in=[1, 7],
    cols_in=['feature_a', 'feature_b'],
    steps_in=14,
    cols_out=['target'],
    steps_out=7,
    augmentation_factor=3,
    noise_std=0.05)

# Pass pre-fit scalers for the test set to prevent leakage
X_test, y_test, _, _, _, _ = dah.prepare_data(
    data=test_df,
    lags_in=[1, 7],
    cols_in=['feature_a', 'feature_b'],
    steps_in=14,
    cols_out=['target'],
    steps_out=7,
    scaler_in=scaler_in,
    scaler_out=scaler_out)

Prophet helpers

from pwml.timeseries import prophethelpers as ph

# Summarise regressor coefficients for a fitted Prophet model
coefs_df = ph.regressor_coefficients(m)

# Plot regressor importance (beta coefficients)
ph.plot_regressors_importance(m, title='Regressor importance')

Visualization

from pwml.timeseries import visualizationhelpers as vh

vh.plot_time_series(
    title='Forecast',
    training=train_df,
    testing=test_df,
    prediction=forecast_df,
    confidence=forecast_df)

vh.plot_time_series_dist(data=residuals, title='Residual distribution')

vh.plot_seasonal_decomposition(data=series, period=52)

vh.plot_autocorrelation(data=series, lags=50)

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utilities

Module	Purpose
graphichelpers	GraphicsStatics: matplotlib/seaborn style initialization, color/linestyle palette, style_plot
mssqlhelpers	execute(proc_name, conn_params, proc_params, commit=True) - call a stored procedure, returns a DataFrame
neptunehelpers	ExperimentManager - Neptune experiment tracking wrapper (neptune-client >= 1.0)
httphelpers	Image download utilities
imagehelpers	PIL image helpers (resize, crop, batch conversion)
filehelpers	Pickle serialization helpers
classificationhelpers	MulticlassClassifierOptimizer - Platt calibration + per-class threshold tuning
commonhelpers	Miscellaneous utilities

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samples - Model Hosting

samples/modelhosting.py is a Flask REST API that serves one or more pre-trained HierarchicalClassifierModel instances.

python samples/modelhosting.py \
    --host 0.0.0.0 \
    --port 5000 \
    --models "v1/division|/path/to/model.joblib"

Each loaded model is exposed at /api/<model-id> (POST). Debug mode is controlled by the FLASK_DEBUG environment variable (default: off). For production use a WSGI server such as gunicorn:

gunicorn -w 4 -b 0.0.0.0:5000 "modelhosting:Statics.g_app"

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Experiment tracking (Neptune)

from pwml.utilities import neptunehelpers as nh

with nh.ExperimentManager(
        log=True,
        project_name='workspace/project',
        experiment_name='run_001',
        experiment_params={'lr': 0.01, 'epochs': 100},
        experiment_tags=['baseline']) as em:

    em.set_experiment_property('dataset_version', 'v3')
    em.log_data_to_neptune(data=results_df, name='results')
    em.log_chart_to_neptune(figure=fig, name='loss_curve')

Requires neptune-client >= 1.0. Set the NEPTUNE_API_TOKEN environment variable before running.