import argparse
import json
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
import warnings

from keras.models import model_from_json
from keras.utils import plot_model
from sklearn.feature_selection.base import SelectorMixin
from sklearn.metrics import precision_recall_curve, average_precision_score
from sklearn.metrics import roc_curve, auc
from sklearn.pipeline import Pipeline
from galaxy_ml.utils import load_model, read_columns, SafeEval


safe_eval = SafeEval()


def main(inputs, infile_estimator=None, infile1=None,
         infile2=None, outfile_result=None,
         outfile_object=None, groups=None,
         ref_seq=None, intervals=None,
         targets=None, fasta_path=None,
         model_config=None):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : str, default is None
        File path to estimator

    infile1 : str, default is None
        File path to dataset containing features or true labels.

    infile2 : str, default is None
        File path to dataset containing target values or predicted
        probabilities.

    outfile_result : str, default is None
        File path to save the results, either cv_results or test result

    outfile_object : str, default is None
        File path to save searchCV object

    groups : str, default is None
        File path to dataset containing groups labels

    ref_seq : str, default is None
        File path to dataset containing genome sequence file

    intervals : str, default is None
        File path to dataset containing interval file

    targets : str, default is None
        File path to dataset compressed target bed file

    fasta_path : str, default is None
        File path to dataset containing fasta file

    model_config : str, default is None
        File path to dataset containing JSON config for neural networks
    """
    warnings.simplefilter('ignore')

    with open(inputs, 'r') as param_handler:
        params = json.load(param_handler)

    title = params['plotting_selection']['title'].strip()
    plot_type = params['plotting_selection']['plot_type']
    if plot_type == 'feature_importances':
        with open(infile_estimator, 'rb') as estimator_handler:
            estimator = load_model(estimator_handler)

        column_option = (params['plotting_selection']
                               ['column_selector_options']
                               ['selected_column_selector_option'])
        if column_option in ['by_index_number', 'all_but_by_index_number',
                             'by_header_name', 'all_but_by_header_name']:
            c = (params['plotting_selection']
                       ['column_selector_options']['col1'])
        else:
            c = None

        _, input_df = read_columns(infile1, c=c,
                                   c_option=column_option,
                                   return_df=True,
                                   sep='\t', header='infer',
                                   parse_dates=True)

        feature_names = input_df.columns.values

        if isinstance(estimator, Pipeline):
            for st in estimator.steps[:-1]:
                if isinstance(st[-1], SelectorMixin):
                    mask = st[-1].get_support()
                    feature_names = feature_names[mask]
            estimator = estimator.steps[-1][-1]

        if hasattr(estimator, 'coef_'):
            coefs = estimator.coef_
        else:
            coefs = getattr(estimator, 'feature_importances_', None)
        if coefs is None:
            raise RuntimeError('The classifier does not expose '
                               '"coef_" or "feature_importances_" '
                               'attributes')

        threshold = params['plotting_selection']['threshold']
        if threshold is not None:
            mask = (coefs > threshold) | (coefs < -threshold)
            coefs = coefs[mask]
            feature_names = feature_names[mask]

        # sort
        indices = np.argsort(coefs)[::-1]

        trace = go.Bar(x=feature_names[indices],
                       y=coefs[indices])
        layout = go.Layout(title=title or "Feature Importances")
        fig = go.Figure(data=[trace], layout=layout)

    elif plot_type == 'pr_curve':
        df1 = pd.read_csv(infile1, sep='\t', header=None)
        df2 = pd.read_csv(infile2, sep='\t', header=None)

        precision = {}
        recall = {}
        ap = {}

        pos_label = params['plotting_selection']['pos_label'].strip() \
            or None
        for col in df1.columns:
            y_true = df1[col].values
            y_score = df2[col].values

            precision[col], recall[col], _ = precision_recall_curve(
                y_true, y_score, pos_label=pos_label)
            ap[col] = average_precision_score(
                y_true, y_score, pos_label=pos_label or 1)

        if len(df1.columns) > 1:
            precision["micro"], recall["micro"], _ = precision_recall_curve(
                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
            ap['micro'] = average_precision_score(
                df1.values, df2.values, average='micro', pos_label=pos_label or 1)

        data = []
        for key in precision.keys():
            trace = go.Scatter(
                x=recall[key],
                y=precision[key],
                mode='lines',
                name='%s (area = %.2f)' % (key, ap[key]) if key == 'micro'
                     else 'column %s (area = %.2f)' % (key, ap[key])
            )
            data.append(trace)

        layout = go.Layout(
            title=title or "Precision-Recall curve",
            xaxis=dict(title='Recall'),
            yaxis=dict(title='Precision')
        )

        fig = go.Figure(data=data, layout=layout)

    elif plot_type == 'roc_curve':
        df1 = pd.read_csv(infile1, sep='\t', header=None)
        df2 = pd.read_csv(infile2, sep='\t', header=None)

        fpr = {}
        tpr = {}
        roc_auc = {}

        pos_label = params['plotting_selection']['pos_label'].strip() \
            or None
        for col in df1.columns:
            y_true = df1[col].values
            y_score = df2[col].values

            fpr[col], tpr[col], _ = roc_curve(
                y_true, y_score, pos_label=pos_label)
            roc_auc[col] = auc(fpr[col], tpr[col])

        if len(df1.columns) > 1:
            fpr["micro"], tpr["micro"], _ = roc_curve(
                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
            roc_auc['micro'] = auc(fpr["micro"], tpr["micro"])

        data = []
        for key in fpr.keys():
            trace = go.Scatter(
                x=fpr[key],
                y=tpr[key],
                mode='lines',
                name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
                     else 'column %s (area = %.2f)' % (key, roc_auc[key])
            )
            data.append(trace)

        trace = go.Scatter(x=[0, 1], y=[0, 1], 
                           mode='lines', 
                           line=dict(color='black', dash='dash'),
                           showlegend=False)
        data.append(trace)

        layout = go.Layout(
            title=title or "Receiver operating characteristic curve",
            xaxis=dict(title='False Positive Rate'),
            yaxis=dict(title='True Positive Rate')
        )

        fig = go.Figure(data=data, layout=layout)

    elif plot_type == 'rfecv_gridscores':
        input_df = pd.read_csv(infile1, sep='\t', header='infer')
        scores = input_df.iloc[:, 0]
        steps = params['plotting_selection']['steps'].strip()
        steps = safe_eval(steps)

        data = go.Scatter(
            x=list(range(len(scores))),
            y=scores,
            text=[str(_) for _ in steps] if steps else None,
            mode='lines'
        )
        layout = go.Layout(
            xaxis=dict(title="Number of features selected"),
            yaxis=dict(title="Cross validation score"),
            title=title or None
        )

        fig = go.Figure(data=[data], layout=layout)

    elif plot_type == 'learning_curve':
        input_df = pd.read_csv(infile1, sep='\t', header='infer')
        plot_std_err = params['plotting_selection']['plot_std_err']
        data1 = go.Scatter(
            x=input_df['train_sizes_abs'],
            y=input_df['mean_train_scores'],
            error_y=dict(
                array=input_df['std_train_scores']
            ) if plot_std_err else None,
            mode='lines',
            name="Train Scores",
        )
        data2 = go.Scatter(
            x=input_df['train_sizes_abs'],
            y=input_df['mean_test_scores'],
            error_y=dict(
                array=input_df['std_test_scores']
            ) if plot_std_err else None,
            mode='lines',
            name="Test Scores",
        )
        layout = dict(
            xaxis=dict(
                title='No. of samples'
            ),
            yaxis=dict(
                title='Performance Score'
            ),
            title=title or 'Learning Curve'
        )
        fig = go.Figure(data=[data1, data2], layout=layout)

    elif plot_type == 'keras_plot_model':
        with open(model_config, 'r') as f:
            model_str = f.read()
        model = model_from_json(model_str)
        plot_model(model, to_file="output.png")
        __import__('os').rename('output.png', 'output')

        return 0

    plotly.offline.plot(fig, filename="output.html",
                        auto_open=False)
    # to be discovered by `from_work_dir`
    __import__('os').rename('output.html', 'output')


if __name__ == '__main__':
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
    aparser.add_argument("-g", "--groups", dest="groups")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-b", "--intervals", dest="intervals")
    aparser.add_argument("-t", "--targets", dest="targets")
    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
    aparser.add_argument("-c", "--model_config", dest="model_config")
    args = aparser.parse_args()

    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
         args.outfile_result, outfile_object=args.outfile_object,
         groups=args.groups, ref_seq=args.ref_seq, intervals=args.intervals,
         targets=args.targets, fasta_path=args.fasta_path,
         model_config=args.model_config)