t-SNEを用いて可視化したデータの取り扱いについて

前提

機械学習の勉強をしています。今回、下記のサイトで写経をしていたときにふと疑問に思ったことを質問します。
https://bunsekikobako.com/t-sne-code-example/

実現したいこと

上記のサイトのコードを写経して画像を出すところまではできました。ここで、ふと思ったのですが、可視化したデータの一部を算出したい、例えば、以下の画像のPerplexity 50のときに緑のプロットが左に固まっているので丸で囲まれたところだけ抽出して値をcsvなどの別のファイル形式に保存するにはどうすればいいのか疑問です。範囲指定をすればできそう...?(-40<ⅹ＜0 and -15<y<20みたいな感じで)
同様にPerplexity 50のときに青色のプロットが右に固まっているので丸で囲まれたところだけを抽出してcsvなどの別のファイル形式に保存したいです。
(画像は標準化した後のものです。コードで言うと、

python
1create_2d_tsne_plots(scaled_X, y, y_items, perplexity_list)

が該当しています。)

該当のソースコード

python
1from mpl_toolkits.mplot3d import axes3d
2import plotly.plotly as py
3import plotly.graph_objs as go
4import os
5from plotly.offline import iplot, init_notebook_mode
6
7def create_2d_tsne_plots(target_X, y, y_labels, perplexity_list= [2, 5, 30, 50, 100]):
8    """
9    args:
10        target_X: pandas.DataFrame. 
11        y: list or series owning label infomation
12        y_labels: labels in y. This is set as argument becaunse only some labels are intended to be ploted.
13        perplexity_list: list of integers. 
14    Returns:
15        None
16    """
17    fig, axes = plt.subplots(nrows=1, ncols=len(perplexity_list),figsize=(5*len(perplexity_list), 4))
18    for i, (ax, perplexity) in enumerate(zip(axes.flatten(), perplexity_list)):
19        start_time = time.time()
20        tsne = TSNE(n_components=2, init='random', random_state=0, perplexity=perplexity)
21        Y = tsne.fit_transform(target_X)
22        for each_label in y_labels:
23            c_plot_bool = y == each_label
24            ax.scatter(Y[c_plot_bool, 0], Y[c_plot_bool, 1], label="{}".format(each_label))
25        end_time = time.time()
26        ax.legend()
27        ax.set_title("Perplexity: {}".format(perplexity))
28        print("Time to plot perplexity {} is {:.2f} seconds.".format(perplexity, end_time - start_time))
29    plt.show()
30    return None
31    
32    
33def create_3d_tsne_plots(target_X, y, y_labels, perplexity_list= [2, 5, 30, 50, 100]):
34    """
35    args:
36        target_X: pandas.DataFrame. 
37        y: list or series owning label infomation
38        y_labels: labels in y. This is set as argument becaunse only some labels are intended to be ploted.
39        perplexity_list: list of integers. 
40    Returns:
41        None
42    """    
43    fig = plt.figure(figsize=(5*len(perplexity_list),4))
44    for i, perplexity in enumerate(perplexity_list):
45        ax = fig.add_subplot(1, len(perplexity_list), i+1, projection='3d')
46        start_time = time.time()
47        tsne = TSNE(n_components=3, init='random', random_state=0, perplexity=perplexity)
48        Y = tsne.fit_transform(target_X)
49        for each_label in y_labels:
50            c_plot_bool = y == each_label
51            ax.scatter(Y[c_plot_bool, 0], Y[c_plot_bool, 1], label="{}".format(each_label))
52        end_time = time.time()
53        ax.legend()
54        ax.set_title("Perplexity: {}".format(perplexity))
55        print("Time to plot perplexity {} is {:.2f} seconds.".format(perplexity, end_time - start_time))
56    plt.show()
57    return None
58    
59    
60def create_single_3d_tsne(target_X, y, y_labels, perplexity, close_plot=True):
61    """
62    args:
63        target_X: pandas.DataFrame. 
64        y: list or series owning label infomation
65        y_labels: labels in y. This is set as argument becaunse only some labels are intended to be ploted.
66        perplexity_list: list of integers. 
67    Returns:
68        Y: target_X transformed to 3d by tsne.
69    """    
70    fig = plt.figure(figsize=(5,5))
71    ax = fig.add_subplot(1,1,1, projection="3d")
72    start_time = time.time()
73    tsne = TSNE(n_components=3, init='random', random_state=0, perplexity=perplexity)
74    Y = tsne.fit_transform(target_X)
75    for each_label in y_labels:
76        c_plot_bool = y == each_label
77        ax.scatter(Y[c_plot_bool, 0], Y[c_plot_bool, 1], label="Quality: {}".format(each_label))
78    end_time = time.time()
79    ax.legend()
80    ax.set_title("Perplexity: {}".format(perplexity))
81    print("Time to plot perplexity {} is {:.2f} seconds.".format(perplexity, end_time - start_time))
82    if close_plot:
83        plt.close()
84    else:
85        plt.show()
86    return Y
87
88def create_single_plotly_3d_scatter(target_df, y, y_labels):
89    init_notebook_mode(connected=True)
90    config={'showLink': False, 'modeBarButtonsToRemove': ['sendDataToCloud','hoverCompareCartesian']}
91
92    data = []
93
94    for each_label in y_labels:
95        c_plot_bool = y == each_label
96        scatter_info = go.Scatter3d(
97                                    x=target_df[c_plot_bool, 0], 
98                                    y=target_df[c_plot_bool,1], 
99                                    z=target_df[c_plot_bool,2],
100                                    mode='markers', 
101                                    marker=dict(
102                                        size=1),
103                                        name="Quality: {}".format(each_label))
104        data.append(scatter_info)
105
106    layout = go.Layout(
107                scene=dict(
108                    xaxis = dict(title="x"),
109                    yaxis=dict(title="y"),
110                    zaxis=dict(title="z")
111                    )
112                )
113
114
115    fig = dict(data = data, layout=layout)
116    iplot(fig, config=config)    
117    return None
118
119create_2d_tsne_plots(X, y, y_items, perplexity_list)
120create_3d_tsne_plots(X, y, y_items, perplexity_list)
121
122#標準化
123scaler = StandardScaler()
124scaled_X = scaler.fit_transform(X)
125
126create_2d_tsne_plots(scaled_X, y, y_items, perplexity_list)
127create_3d_tsne_plots(scaled_X, y, y_items, perplexity_list)
128