<jupyter_start><jupyter_text>Holy crap, it parsed my terrible file perfectly.<jupyter_code>df2 = pd.read_csv('../Tuning_VBounds/analysis/raw_data.csv', sep=',')
df2
dfFull = pd.concat([df, df2], axis=1)
dfFull.head()
dfFull.columns
newcols = []
for col in dfFull.columns:
    newcols.append(col.strip())
dfFull.columns = newcols
dfFull.columns
plt.scatter(dfFull['Ratio'], dfFull['test_accuracy'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TEST Score')
plt.title('Test Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_SCAT_test_score_ratio.svg')
dfFull['Ratio'].corr(dfFull['test_accuracy'])
plt.scatter(dfFull['Ratio'], dfFull['training_accuracy'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_SCAT_train_score_ratio.svg')
dfFull['Ratio'].corr(dfFull['training_accuracy'])
plt.scatter(dfFull['test_accuracy'], dfFull['training_accuracy'])
plt.xlabel('TEST Score')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Test Accuracy')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_SCAT_train_score_test_score.svg')
dfFull['test_accuracy'].corr(dfFull['training_accuracy'])
dfFull[dfFull['training_accuracy'] < 0.76]['Ratio'].corr(dfFull[dfFull['training_accuracy'] < 0.76]['training_accuracy'])
dfFull[dfFull['training_accuracy'] < 0.76]['Ratio'].corr(dfFull[dfFull['training_accuracy'] < 0.76]['test_accuracy'])
plt.scatter(dfFull[dfFull['training_accuracy'] < 0.76]['Ratio'], dfFull[dfFull['training_accuracy'] < 0.76]['test_accuracy'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TEST Score')
plt.title('Test Accuracy vs. Hits Ratio - Subset : Train Score < 0.76')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_SCAT_SUBSET_test_score_ratio.svg')
dfFull[dfFull['training_accuracy'] < 0.76]
(dfFull[dfFull['training_accuracy'] < 0.76] == dfFull[(dfFull['vmax']+dfFull['vmin'])/2 >= 0.0]).all()
dfFull[dfFull['training_accuracy'] < 0.76]['test_accuracy'].corr(dfFull[dfFull['training_accuracy'] < 0.76]['training_accuracy'])
dfFull[dfFull['training_accuracy'] > 0.76]['test_accuracy'].corr(dfFull[dfFull['training_accuracy'] > 0.76]['training_accuracy'])
dfFull[dfFull['training_accuracy'] > 0.76]['test_accuracy'].corr(dfFull[dfFull['training_accuracy'] > 0.76]['Ratio'])
dfFull[dfFull['training_accuracy'] > 0.76]['Ratio'].corr(dfFull[dfFull['training_accuracy'] > 0.76]['training_accuracy'])<jupyter_output><empty_output><jupyter_text>---<jupyter_code>dfneg2 = pd.read_csv('../Tuning_VBounds_Range/vbounds_range_center-2/analysis/even_split_results.txt', skiprows=[1], sep = ':')
dfneg2
dfneg2.dtypes
dfneg2_2 = pd.read_csv('../Tuning_VBounds_Range/vbounds_range_center-2/analysis/raw_data.csv', sep=',')
dfneg2_2
dfneg2Full = pd.concat([dfneg2, dfneg2_2], axis=1)
dfneg2Full.head()
dfneg2Full.drop(0, axis=0, inplace=True)
dfneg2Full.head(1)
dfneg2Full.columns
newcols = []
for col in dfneg2Full.columns:
    newcols.append(col.strip())
dfneg2Full.columns = newcols
dfneg2Full.columns
plt.scatter(dfneg2Full['Ratio'], dfneg2Full['test_score'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TEST Score')
plt.title('Test Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_-2.0_SCAT_test_score_ratio.svg')
dfneg2Full['Ratio'].corr(dfneg2Full['test_score'])
plt.scatter(dfneg2Full['Ratio'], dfneg2Full['training_score'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_-2.0_SCAT_train_score_ratio.svg')
dfneg2Full['Ratio'].corr(dfneg2Full['training_score'])
dfneg2subset = dfneg2Full[(dfneg2Full['vmax']+dfneg2Full['vmin']) / 2 > 0.0]
dfneg2subset
plt.scatter(dfneg2Full['test_score'], dfneg2Full['training_score'])
plt.xlabel('TEST Score')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Test Accuracy')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_-2.0_SCAT_train_score_test_score.svg')
dfneg2Full['test_score'].corr(dfneg2Full['training_score'])<jupyter_output><empty_output><jupyter_text>In the first dataset I looked at, there was a perfect correlation between test_accuracy, training_accuracy, and Ratio as long as the vcenter > 0.0. If vcenter < 0.0, there was no correlation between training_accuracy and test_accuracy and small to moderate correlations between training/test_accuracy and Ratio. In the second dataset, NONE of the observations had vcenter < 0.0. However, the correlations were still moderate, but very different than the first dataset.  Third Dataset - vcenter = 2.0<jupyter_code>dfpos2 = pd.read_csv('../Tuning_VBounds_Range/vbounds_range_center2/analysis/even_split_results.txt', skiprows=[1], sep = ':')
dfpos2
dfpos2.dtypes
dfpos2_2 = pd.read_csv('../Tuning_VBounds_Range/vbounds_range_center2/analysis/raw_data.csv', sep=',')
dfpos2_2
dfpos2Full = pd.concat([dfpos2, dfpos2_2], axis=1)
dfpos2Full.head()
dfpos2Full.drop(0, axis=0, inplace=True)
dfpos2Full.head(1)
dfpos2Full.columns
newcols = []
for col in dfpos2Full.columns:
    newcols.append(col.strip())
dfpos2Full.columns = newcols
dfpos2Full.columns
plt.scatter(dfpos2Full['Ratio'], dfpos2Full['test_score'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TEST Score')
plt.title('Test Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_+2.0_SCAT_test_score_ratio.svg')
dfpos2Full['Ratio'].corr(dfpos2Full['test_score'])
plt.scatter(dfpos2Full['Ratio'], dfpos2Full['training_score'])
plt.xlabel('Hits/Total Labels')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Fraction of Hits in y_train')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_+2.0_SCAT_train_score_ratio.svg')
dfpos2Full['Ratio'].corr(dfpos2Full['training_score'])
plt.scatter(dfpos2Full['test_score'], dfpos2Full['training_score'])
plt.xlabel('TEST Score')
plt.ylabel('TRAIN Score')
plt.title('Training Accuracy vs. Test Score')
plt.savefig('../lab_notebooks/cfs_notebook_files/Even_Split_Check_vcenter_+2.0_SCAT_train_score_test_score.svg')
dfpos2Full['test_score'].corr(dfpos2Full['training_score'])
dfpos2Full[(dfpos2Full['vmax']+dfpos2Full['vmin']) / 2 > 0.0]<jupyter_output><empty_output>
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