<jupyter_start><jupyter_text>Gráfica<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use(['ggplot','seaborn-paper'])
size = (8, 7)
a4_dims = size
palette_3 = sns.color_palette("mako_r",3)<jupyter_output><empty_output><jupyter_text>LR<jupyter_code>fig, ax = plt.subplots(figsize=a4_dims)
ax = sns.lineplot(x="Sample Number", y="F1 Micro", legend="full", hue="Resolution", palette=palette_3,
                  data=df_LR)
#sns.scatterplot
#plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#ax.set_xlim(-2.5,3.5)
#plt.savefig('./results/graphics/ST_LR.pdf', format='pdf', bbox_inches='tight')
plt.axvline(590, 0, 1, linestyle='--', color='black')
plt.axvline(590*2, 0, 1, linestyle='--', color='black')
plt.axvline(590*3, 0, 1, linestyle='--', color='black')
plt.axvline(590*4, 0, 1, linestyle='--', color='black')
plt.ylabel('$F_1$ Score')
plt.xlabel('Training Sample Size')
plt.savefig('./results/graphics/DA_LR.pdf', format='pdf', bbox_inches='tight')
plt.show()
fig, ax = plt.subplots(figsize=a4_dims)
ax = sns.lineplot(x="Sample Number", y="Precision Micro", legend="full", hue="Resolution", palette=palette_3,
                  data=df_LR)
#sns.scatterplot
#plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#ax.set_xlim(-2.5,3.5)
#plt.savefig('./results/graphics/ST_LR.pdf', format='pdf', bbox_inches='tight')
plt.axvline(590, 0, 1, linestyle='--', color='black')
plt.axvline(590*2, 0, 1, linestyle='--', color='black')
plt.axvline(590*3, 0, 1, linestyle='--', color='black')
plt.axvline(590*4, 0, 1, linestyle='--', color='black')
plt.show()<jupyter_output><empty_output><jupyter_text>SVC<jupyter_code>fig, ax = plt.subplots(figsize=a4_dims)
ax = sns.lineplot(x="Sample Number", y="F1 Micro", legend="full", hue="Resolution", palette=palette_3,
                  data=df_SVC)
#sns.scatterplot
#plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#ax.set_xlim(-2.5,3.5)
#plt.savefig('./results/graphics/ST_LR.pdf', format='pdf', bbox_inches='tight')
plt.axvline(590, 0, 1, linestyle='--', color='black')
plt.axvline(590*2, 0, 1, linestyle='--', color='black')
plt.axvline(590*3, 0, 1, linestyle='--', color='black')
plt.axvline(590*4, 0, 1, linestyle='--', color='black')
plt.ylabel('$F_1$ Score')
plt.xlabel('Training Sample Size')
plt.savefig('./results/graphics/DA_SVC.pdf', format='pdf', bbox_inches='tight')
plt.show()<jupyter_output><empty_output><jupyter_text>RF<jupyter_code>fig, ax = plt.subplots(figsize=a4_dims)
ax = sns.lineplot(x="Sample Number", y="F1 Micro", legend="full", hue="Resolution", palette=palette_3,
                  data=df_RF)
#sns.scatterplot
#plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#ax.set_xlim(-2.5,3.5)
#plt.savefig('./results/graphics/ST_LR.pdf', format='pdf', bbox_inches='tight')
plt.axvline(590, 0, 1, linestyle='--', color='black')
plt.axvline(590*2, 0, 1, linestyle='--', color='black')
plt.axvline(590*3, 0, 1, linestyle='--', color='black')
plt.axvline(590*4, 0, 1, linestyle='--', color='black')
plt.ylabel('$F_1$ Score')
plt.xlabel('Training Sample Size')
plt.savefig('./results/graphics/DA_RF.pdf', format='pdf', bbox_inches='tight')
plt.show()
fig, ax = plt.subplots(figsize=a4_dims)
ax = sns.lineplot(x="Sample Number", y="F1 Macro", legend="full", hue="Resolution", palette=palette_3,
                  data=df_RF)
#sns.scatterplot
#plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#ax.set_xlim(-2.5,3.5)
#plt.savefig('./results/graphics/ST_LR.pdf', format='pdf', bbox_inches='tight')
plt.axvline(590, 0, 1, linestyle='--', color='black')
plt.axvline(590*2, 0, 1, linestyle='--', color='black')
plt.axvline(590*3, 0, 1, linestyle='--', color='black')
plt.axvline(590*4, 0, 1, linestyle='--', color='black')
plt.show()<jupyter_output><empty_output>
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