<jupyter_start><jupyter_text>Convergence Results<jupyter_code>res2=z_top.groupby(["w_nc","trial"]).head(10)
res2.to_csv("top_norms_10_result_exp5_w_nc.csv")
#res2
z_top.groupby(["w_nc","trial"]).head(5).to_csv("top_norms_every_exp.csv")
z_top.loc[z_top.w_nc==0.25].groupby(["w_nc","trial"]).head(2)
import seaborn as sns
fig, axs = plt.subplots(5,5,figsize=(7,10),dpi=200):
    z_top.loc[z_top.w_nc==0.0]
    sns.displot(grp.reindex(),x="trial", y=('log_posterior',  'mean'), hue="trial",\
            kind="hist",palette=sns.color_palette("husl", 3),dpi=300)
import seaborn as sns
import matplotlib.pyplot as plt
sns.displot(z_top.loc[z_top.w_nc.isin([0.25,0.05])].reindex(),x="w_nc", y=('log_posterior',  'mean'), hue="trial",\
            kind="hist",palette=sns.color_palette("husl", 3))
sns_plot=sns.displot(data=z_top.loc[z_top.w_nc.isin([0.05,0.35])],\
                     y=('log_posterior',  'mean'),row="w_nc",col="trial", kind="kde")
sns_plot.savefig("conv_arg.jpg",dpi=300)
import seaborn as sns
ax=sns.displot(z_top.loc[z_top.w_nc<0.25].reindex(),x="trial", y=('log_posterior',  'mean'), hue="trial",\
            kind="hist",palette=sns.color_palette("husl", 3))
z_top.reindex().columns
loc[z_top.w_nc==0.0]<jupyter_output><empty_output><jupyter_text>Collate convergence<jupyter_code>!touch "exp5_convergence.txt"
for exp_path in exp_paths:
    #w_nc,trial=tuple(map(lambda x: float(x.split("=")[-1]),exp_path.split(",")))
    !echo {exp_path} >> exp5_convergence.txt
    !cat {base_path+exp_path+"/conv_test_nc.txt"} >> exp5_convergence.txt
    !echo -e "\n ---" >> exp5_convergence.txt<jupyter_output><empty_output><jupyter_text>Cp cnc_movement images<jupyter_code>for exp_path in exp_paths:
    !cp {base_path+exp_path+"/cnc_movement.jpg"} ../../ICJAI21/exp5/{exp_path+",cnc_movmt.jpg"}<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup