Add pearson and spearman correlation

correlation.py

@@ -8,8 +8,11 @@ import os
 #sns.scatterplot(data=sing_fanfic, x="grishaverse_good", y=sing_fanfic.index)
 #plt.savefig("cosmere/fanfiction_stylo_data/deltas/grishaverse_good.png")
 
-types_of_feature = ["data_overview", "md_freq_dist", "tag_freq_dist","punct_tag_freq_dist", "sent_len_dist", "tk_len_dist",]
-fandoms = ["call_me_by_your_name"]
+#types_of_feature = ["data_overview", "md_freq_dist", "tag_freq_dist","punct_tag_freq_dist", "sent_len_dist", "tk_len_dist",]
+types_of_feature = ["data_overview","sent_len_dist", "tk_len_dist","punct_tag_freq_dist", "md_freq_dist", "tag_freq_dist", "pronouns_dist"] #"data_overview",
+fandoms = ['call_me_by_your_name', 'cosmere', 'divergent', 'grishaverse', 'maze_runner', 'murderbot', 'percy', 'red_white_royal_blue', 'school_for_good_and_evil', 'simonverse', 'song_of_achilles', 'throne_of_glass',]
+   
+#fandoms = ["call_me_by_your_name"]
 # deltas
 for fandom in fandoms:
     print(f"{fandom}")
@@ -41,6 +44,9 @@ for fandom in fandoms:
     df_pearsons = df_pearsons.T
     df_spearman = df_spearman.T
 
+    data_path = f"correlation/deltas/{fandom}"
+    if os.path.exists(data_path) == False: os.makedirs(data_path)
+
     df_pearsons.to_csv(f"correlation/deltas/{fandom}/pearsons.csv")
     df_spearman.to_csv(f"correlation/deltas/{fandom}/spearman.csv")