73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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import numpy as np
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from sklearn.manifold import TSNE
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from matplotlib import pyplot as plt
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from sklearn.cluster import AgglomerativeClustering
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import plotly.express as px
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from constants import *
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from data import load_data, fit_model, create_data
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def show_plots(y, x_axis, y_axis, plot_name):
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only_labels = y.to_list()
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labels = {
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"x": "Price",
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"y": "Value",
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}
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fig = px.scatter(None, x=x_axis, y=y_axis, labels=labels, opacity=1, color=only_labels)
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fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey',
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zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',
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showline=True, linewidth=1, linecolor='black')
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fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey',
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zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',
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showline=True, linewidth=1, linecolor='black')
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fig.update_layout(title_text=plot_name, plot_bgcolor='white')
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fig.update_traces(marker=dict(size=4))
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fig.write_html(f'images/image.html')
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def build_launch_name(filename: str, model: TSNE):
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data = [
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filename, ' t-SNE ',
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'learning_rate=', str(model.learning_rate), ' ',
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'perplexity=', str(model.perplexity), ' ',
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'early_exaggeration=', str(model.early_exaggeration), ' ',
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'n_iter=', str(model.n_iter), ' ',
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'n_iter_without_progress=', str(model.n_iter_without_progress), ' ',
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'min_grad_norm=', str(model.min_grad_norm), ' ',
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'metric=', str(model.metric), ' ',
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'init=', str(model.init), ' ',
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'method=', str(model.method), ' ',
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'angle=', str(model.angle),
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]
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return ''.join(data)
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def launch(filename, X, Y, model: TSNE):
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x_axis, y_axis = fit_model(model, X)
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plot_name = build_launch_name(filename, model)
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show_plots(Y, x_axis, y_axis, plot_name=plot_name)
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def draw_experimental_clusters():
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data = create_data()
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cluster = AgglomerativeClustering(n_clusters=NUMBER_OF_CLUSTERS, affinity='euclidean', linkage='ward')
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plt.xlabel("Год", fontweight="bold")
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plt.ylabel("Цена", fontweight="bold")
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corr = np.array(data)
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cluster.fit_predict(corr)
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plt.scatter(corr[:, 0], corr[:, 1], c=cluster.labels_, cmap='rainbow')
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plt.show()
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def tnse_cluster():
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X, Y = load_data()
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model = TSNE(n_components=2, learning_rate=200, perplexity=50, early_exaggeration=6, angle=0.1)
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launch(filename='true_car_listings.csv', X=X, Y=Y, model=model)
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