# In[9]: pd.DataFrame(_scaled, columns=df.columns).head() # ### Biplot # # A scatterplot projected onto the first two principal components. # In[10]: plt.figure() data_scaled = pd.DataFrame(_scaled, columns=df.columns) triplot(pca, data_scaled, title='ANES 2012 Biplot', color=data_scaled.PartyID) # In[11]: biplot(pca, data_scaled, title='ANES 2012 Biplot', color=data_scaled.PartyID) # Sure, all of the original axes are negative in the first component. That's okay! To quote Dr. Eric Larson: # > Because all the data is somewhat correlated, giving a mostly unidimensional representation. Positive/negative isn't so important because eigenvectors could theoretically start anywhere--but traditionally we use the origin. # # **Update:** The demographic factor of education level has a different sign from the others. # In[12]: def fpc_ordered(corr):
#
# How much of the variance in the data is explained by each successive component?
# In[11]:
plot_explained_variance(pca)
# ### Biplot
#
# A scatterplot projected onto the first two principal components.
# In[12]:
data_scaled = pd.DataFrame(_scaled, columns=df.columns)
triplot(pca,
data_scaled,
title='ANES {} Biplot'.format(YEAR),
color=data_scaled.PartyID)
# In[13]:
biplot(pca,
data_scaled,
title='ANES {} Biplot'.format(YEAR),
color=data_scaled.PartyID)
# In[14]:
pca.explained_variance_
# ## Dropping na
plt.ylabel("2nd component")
plt.figure() # plt.subplot(4, 1, 3, aspect='equal')
plt.plot(X_poly[reds, 0], X_poly[reds, 1], "r.")
plt.plot(X_poly[blues, 0], X_poly[blues, 1], "b.")
plt.title("Projection by KPCA")
plt.xlabel("1st principal component in space induced by $\phi$")
plt.ylabel("2nd component")
plt.figure() # plt.subplot(4, 1, 4, aspect='equal')
plt.plot(X_pback[reds, 0], X_pback[reds, 1], "r.")
plt.plot(X_pback[blues, 0], X_pback[blues, 1], "b.")
plt.title("Original space after inverse transform")
plt.xlabel("$x_1$")
plt.ylabel("$x_2$")
# In[14]:
df.pid_self.value_counts()
# In[15]:
df.postvote_presvtwho.value_counts()
# In[19]:
data = pd.DataFrame(X, columns=df.columns)
triplot(pca, data)
# In[ ]:
