tSNE data

tSNETest.py

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+import pandas as pd
+from sklearn.manifold import TSNE
+import seaborn as sns
+from matplotlib import pyplot as plt
+import os
+
+#Make sure working directory is the same!
+os.getcwd()
+os.chdir("C:\\Users\\Jonathan\\Desktop\BPS - RP1\\January\Week 12 - 11-01-2021\\15 January")
+
+df = pd.read_csv('output_data_warrior.csv', error_bad_lines=False)
+# print(df.shape)
+df_select = df[["rmsd1", "rmsd2"]]
+# print(df_select)
+
+# Text below was a try to show fingerprints (still possible, but unsure what use the plot would have)
+# df_length = len(df_select)
+# finger = Fingerprints[5]
+# print(finger)
+
+# for x in range(df_length):
+#     finger = Fingerprints[x]
+#     df_select = df_select[x].update(finger)
+
+# df_select = df_select.apply (pd.to_numeric, errors='coerce')
+# df_select = df_select.dropna()
+# df_select = df_select.apply (pd.to_numeric, errors='coerce')
+# df_select = df_select.dropna()
+# print(df_select)
+
+#Sets out the 2 selected columns from df_select against each other
+m = TSNE(learning_rate=40)
+tsne_features = m.fit_transform(df_select)
+tsne_features[1:4, :]
+
+df['tsne-2d-one']=tsne_features[:,0]
+df['tsne-2d-two']=tsne_features[:,1]
+
+plt.figure(figsize=(16,10))
+sns.scatterplot(
+    x="tsne-2d-one", y="tsne-2d-two",
+    hue="tsne-2d-two",
+    palette=sns.color_palette("flare", as_cmap=True),
+    data=df,
+    legend="brief",
+    alpha=0.7
+)
+print("Created plot!")
+
+# sns.scatterplot(x="x", y="y", hue="y", palette=sns.color_palette("hls", 2), data=df, legend="full")
+# plot.show()