"""
Linear Model for Multi-label Classification.
============================================

This guide will take you through how LibMultiLabel can
be used to train a linear classifier in python scripts.

For this guide, we will only need the linear module:
"""

import libmultilabel.linear as linear

######################################################################
# To start, we need to read and preprocess the input data:

datasets = linear.load_dataset("txt", "data/rcv1/train.txt", "data/rcv1/test.txt")
preprocessor = linear.Preprocessor()
preprocessor.fit(datasets)
datasets = preprocessor.transform(datasets)

# Alternatively, you can fit and transform the dataset at the same time:
# datasets = preprocessor.fit_transform(datasets)


######################################################################
# .. note::
#   The ``fit`` method calculates the parameters (such as label mapping) that will be used for later transformation
#   from the given dataset.
#
#   The ``transform`` method applies the learned parameters to the given dataset and returns a new transformed dataset.
#
# The preprocessor handles many issues such as: mapping
# the labels into indices and transforming textual data to
# numerical data. The loaded dataset has the structure::
#
#     {
#         'train': {
#             'x': # training features
#             'y': # training labels
#         },
#         'test': {
#             'x': # test features
#             'y': # test labels
#         },
#     }
#
# Next we train the model:

model = linear.train_1vsrest(datasets["train"]["y"], datasets["train"]["x"], "")

######################################################################
# The third argument is the options string for
# `LibLinear <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__.
# We may leave it as the default for now.
#
# Once we have the model, we may predict with it:

preds = linear.predict_values(model, datasets["test"]["x"])

######################################################################
# ``preds`` holds the decision values, i.e. the raw values
# outputted by the model. To transform it into predictions,
# you can apply the API ``get_positive_labels`` to get predicted labels and their corresponding scores
# by using ``label_mapping`` in ``preprocessor`` and ``preds`` from the last step.

pred_labels, pred_scores = linear.get_positive_labels(preds, preprocessor.label_mapping)

######################################################################
# We now have the labels (``pred_labels``) and scores (``pred_scores``).
# You can use the following code to save the prediction to a list.

prediction = []
for label, score in zip(pred_labels, pred_scores):
    prediction.append([f"{i}:{s:.4}" for i, s in zip(label, score)])

######################################################################
# The first instance looks like:
#
#   >>> print(prediction[0])
#   ...
#       ['GCAT:1.345', 'GSPO:1.519']
#
# To see how well we performed, we may want to check various
# metrics with the test set.
# Since the dataset we loaded are stored as ``scipy.sparse.csr_matrix``,
# we will first transform the dataset to ``np.array``.

target = datasets["test"]["y"].toarray()

##############################################################################
# Then we will compute the metrics with ``compute_metrics``.

metrics = linear.compute_metrics(
    preds,
    target,
    monitor_metrics=["Macro-F1", "Micro-F1", "P@1", "P@3", "P@5"],
)

print(metrics)

######################################################################
# The results will look similar to::
#
#     {'Macro-F1': 0.5171960144875225, 'Micro-F1': 0.8008124243391698, 'P@1': 0.9573153795447128, 'P@3': 0.799074151109632, 'P@5': 0.5579924865442584}