<jupyter_start><jupyter_text>**NOTE:** The 0th row failed because there was an error checking statement in `cpso.py` that prevented it from being called with a vbounds range of 0.<jupyter_code>df.tail()<jupyter_output><empty_output><jupyter_text>**NOTE:** The final range is not 8.0 as designed, but 7.6. Upon checking `experiment_script.py`, it looks like I forgot that the stop argument of `np.linspace` is excluded. Both of the **NOTE**s so far are experimental error. However, I do not think the loss of this data will impact the results, so I am moving forward with the analysis.<jupyter_code>df.drop(0, axis=0, inplace=True)
df.head()
df.loc[:, ['vrange', 'num_of_features', 'a_fitness', 'training_score', 'test_score']]
df.loc[:, ['vrange', 'num_of_features', 'a_fitness', 'training_score', 'test_score']].describe()
sns.pairplot(df.loc[:, ['vrange', 'num_of_features', 'a_fitness', 'training_score', 'test_score']])
plt.savefig('../../../lab_notebooks/cfs_notebook_files/Tuning_VBounds_Range_vcenter_-4.0_pairplot.svg')<jupyter_output><empty_output><jupyter_text>As expected, the number of features is extremely low without exception. The v_bounds for this experiment ranged from (-4.4, -3.6) to (-7.8, -0.2). Thus velocities were always negative and position vectors were guaranteed to continually decrease. No relation between test_score and vrange. Interestingly, fitness and training_score appear to be somewhat robust to range. Interested to see if this holds up in the other 4 cohorts. I predict that it will in vcenter=4.0, less so in -2 and 2 and least in 2.<jupyter_code>df.corr(method='pearson')<jupyter_output><empty_output>
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