#!/usr/bin/env python

from sklearn.cluster import DBSCAN as _DBSCAN

import cexc
from base import BaseAlgo, ClustererMixin
from util import df_util
from util.param_util import convert_params


class DBSCAN(ClustererMixin, BaseAlgo):
    def __init__(self, options):
        self.handle_options(options)
        out_params = convert_params(
            options.get('params', {}), floats=['eps'], ints=['min_samples']
        )

        self.estimator = _DBSCAN(**out_params)

    def fit(self, df, options):
        # Make a copy of data, to not alter original dataframe
        X = df.copy()

        X, nans, _ = df_util.prepare_features(
            X=X, variables=self.feature_variables, mlspl_limits=options.get('mlspl_limits')
        )
        if nans.any():
            # If null values found in the data, warn the user to handle them before fit.
            cexc.messages.warn(
                "NULL values found in the dataset. Clusters are not assigned for these values currently. "
                "Please consider handling null (or missing) entries to get appropriate clustering output."
            )
        y_hat = self.estimator.fit_predict(X.values)

        default_name = 'cluster'
        output_name = options.get('output_name', default_name)

        output = df_util.create_output_dataframe(
            y_hat=y_hat, nans=nans, output_names=output_name
        )
        df = df_util.merge_predictions(df, output)
        return df