import numpy as np
from sklearn.manifold import TSNE
from matplotlib import pyplot as plt
from sklearn.cluster import AgglomerativeClustering
import plotly.express as px
from constants import *
from data import load_data, fit_model, create_data


def show_plots(y, x_axis, y_axis, plot_name):
    only_labels = y.to_list()

    labels = {
        "x": "Price",
        "y": "Value",
    }

    fig = px.scatter(None, x=x_axis, y=y_axis, labels=labels, opacity=1, color=only_labels)
    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey',
                     zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',
                     showline=True, linewidth=1, linecolor='black')
    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey',
                     zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',
                     showline=True, linewidth=1, linecolor='black')

    fig.update_layout(title_text=plot_name, plot_bgcolor='white')
    fig.update_traces(marker=dict(size=4))

    fig.write_html(f'images/image.html')


def build_launch_name(filename: str, model: TSNE):
    data = [
        filename, ' t-SNE ',
        'learning_rate=', str(model.learning_rate), ' ',
        'perplexity=', str(model.perplexity), ' ',
        'early_exaggeration=', str(model.early_exaggeration), ' ',
        'n_iter=', str(model.n_iter), ' ',
        'n_iter_without_progress=', str(model.n_iter_without_progress), ' ',
        'min_grad_norm=', str(model.min_grad_norm), ' ',
        'metric=', str(model.metric), ' ',
        'init=', str(model.init), ' ',
        'method=', str(model.method), ' ',
        'angle=', str(model.angle),
    ]
    return ''.join(data)


def launch(filename, X, Y, model: TSNE):
    x_axis, y_axis = fit_model(model, X)
    plot_name = build_launch_name(filename, model)
    show_plots(Y, x_axis, y_axis, plot_name=plot_name)


def draw_experimental_clusters():
    data = create_data()
    cluster = AgglomerativeClustering(n_clusters=NUMBER_OF_CLUSTERS, affinity='euclidean', linkage='ward')

    plt.xlabel("Год", fontweight="bold")
    plt.ylabel("Цена", fontweight="bold")

    corr = np.array(data)
    cluster.fit_predict(corr)

    plt.scatter(corr[:, 0], corr[:, 1], c=cluster.labels_, cmap='rainbow')
    plt.show()


def tnse_cluster():
    X, Y = load_data()
    model = TSNE(n_components=2, learning_rate=200, perplexity=50, early_exaggeration=6, angle=0.1)
    launch(filename='true_car_listings.csv', X=X, Y=Y, model=model)