Usage Sample
''''''''''''
.. code:: python
from model_wrapper import SplitClassModelWrapper
classes = ['class1', 'class2', 'class3'...]
X = [[...], [...],]
y = [0, 0, 1, 2, 1...]
model = ...
wrapper = SplitClassModelWrapper(model, classes=classes)
wrapper.train(X, y, val_size=0.2)
X_test = [[...], [...],]
y_test = [0, 1, 1, 2, 1...]
result = wrapper.evaluate(X_test, y_test)
# 0.953125
result = wrapper.predict(X_test)
# [0, 1]
result = wrapper.predict_classes(X_test)
# ['class1', 'class2']
result = wrapper.predict_proba(X_test)
# ([0, 1], array([0.99439645, 0.99190724], dtype=float32))
result = wrapper.predict_classes_proba(X_test)
# (['class1', 'class2'], array([0.99439645, 0.99190724], dtype=float32))
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"description": "Usage Sample\n''''''''''''\n\n.. code:: python\n\n from model_wrapper import SplitClassModelWrapper\n\n classes = ['class1', 'class2', 'class3'...]\n X = [[...], [...],]\n y = [0, 0, 1, 2, 1...]\n\n model = ...\n wrapper = SplitClassModelWrapper(model, classes=classes)\n wrapper.train(X, y, val_size=0.2)\n\n X_test = [[...], [...],]\n y_test = [0, 1, 1, 2, 1...]\n result = wrapper.evaluate(X_test, y_test)\n # 0.953125\n\n result = wrapper.predict(X_test)\n # [0, 1]\n\n result = wrapper.predict_classes(X_test)\n # ['class1', 'class2']\n\n result = wrapper.predict_proba(X_test)\n # ([0, 1], array([0.99439645, 0.99190724], dtype=float32))\n\n result = wrapper.predict_classes_proba(X_test)\n # (['class1', 'class2'], array([0.99439645, 0.99190724], dtype=float32))\n\n\n",
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