tra-analysis/data analysis/visualization.py

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2018-12-21 03:45:05 +00:00
#Titan Robotics Team 2022: Visualization Module
#Written by Arthur Lu & Jacob Levine
#Notes:
# this should be imported as a python module using 'import visualization'
# this should be included in the local directory or environment variable
# this module has not been optimized for multhreaded computing
#Number of easter eggs: Jake is Jewish and does not observe easter.
#setup:
__version__ = "1.0.0.001"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.0.xxx:
-added basic plotting, clustering, and regression comparisons"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>, "
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'affinity_prop',
'bar_graph',
'dbscan',
'kmeans',
'line_plot',
'pca_comp',
'regression_comp',
'scatter_plot',
'spectral',
'vis_2d'
]
#imports
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
import statistics
#bar of x,y
def bar_graph(x,y):
x=np.asarray(x)
y=np.asarray(y)
plt.bar(x,y)
plt.show()
#scatter of x,y
def scatter_plot(x,y):
x=np.asarray(x)
y=np.asarray(y)
plt.scatter(x,y)
plt.show()
#line of x,y
def line_plot(x,y):
x=np.asarray(x)
y=np.asarray(y)
plt.scatter(x,y)
plt.show()
#plot data + regression fit
def regression_comp(x,y,reg):
x=np.asarray(x)
y=np.asarray(y)
regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000))
regy=[]
for i in regx:
regy.append(eval(reg[0].replace("z",str(i))))
regy=np.asarray(regy)
plt.scatter(x,y)
plt.plot(regx,regy,color="orange",linewidth=3)
plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
u"R\u00b2="+str(round(reg[2],5)),
horizontalalignment='center', verticalalignment='center')
plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
"MSE="+str(round(reg[1],5)),
horizontalalignment='center', verticalalignment='center')
plt.show()
#PCA to compress down to 2d
def pca_comp(big_multidim):
pca=PCA(n_components=2)
td_norm=StandardScaler().fit_transform(big_multidim)
td_pca=pca.fit_transform(td_norm)
return td_pca
#one-stop visualization of multidim datasets
def vis_2d(big_multidim):
td_pca=pca_comp(big_multidim)
plt.scatter(td_pca[:,0], td_pca[:,1])
def cluster_vis(data, cluster_assign):
pca=PCA(n_components=2)
td_norm=StandardScaler().fit_transform(data)
td_pca=pca.fit_transform(td_norm)
colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']),
int(max(clu) + 1))))
colors = np.append(colors, ["#000000"])
plt.figure(figsize=(8, 8))
plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
plt.show()
#affinity prop- slow, but ok if you don't have any idea how many you want
def affinity_prop(data, damping=.77, preference=-70):
td_norm=StandardScaler().fit_transform(data)
db = AffinityPropagation(damping=damping,preference=preference).fit(td)
y=db.predict(td_norm)
return y
#DBSCAN- slightly faster but can label your dataset as all outliers
def dbscan(data, eps=.3):
td_norm=StandardScaler().fit_transform(data)
db = DBSCAN(eps=eps).fit(td)
y=db.labels_.astype(np.int)
return y
#K-means clustering- the classic
def kmeans(data, num_clusters):
td_norm=StandardScaler().fit_transform(data)
db = KMeans(n_clusters=num_clusters).fit(td)
y=db.labels_.astype(np.int)
return y
#Spectral Clustering- Seems to work really well
def spectral(data, num_clusters):
td_norm=StandardScaler().fit_transform(data)
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db = SpectralClustering(n_clusters=num_clusters, eigen_solver='arpack',
affinity="nearest_neighbors").fit(td)
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y=db.labels_.astype(np.int)
return y