Unsupervised Learning in Python--Visualization with hierarchical clustering and t-SNE.py

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 29 16:21:02 2019

@author: z
"""

###############################################################################Unsupervised Learning in Python
###Hierarchical clustering of the grain data
# Perform the necessary imports
from scipy.cluster.hierarchy import linkage ,dendrogram
import matplotlib.pyplot as plt                                                                                   

# Calculate the linkage: mergings
mergings = linkage(samples, method='complete')

# Plot the dendrogram, using varieties as labels
dendrogram(mergings,
           labels=varieties,
           leaf_rotation=90,
           leaf_font_size=6,
)
plt.show()



###Hierarchies of stocks
# Import normalize
from sklearn.preprocessing import normalize

# Normalize the movements: normalized_movements
normalized_movements = normalize(movements)

# Calculate the linkage: mergings
mergings = linkage(normalized_movements, method='complete')

# Plot the dendrogram
dendrogram(mergings, labels=companies, leaf_rotation=90, leaf_font_size=6)
plt.show()



###Different linkage, different hierarchical clustering!
# Perform the necessary imports
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import linkage, dendrogram

# Calculate the linkage: mergings
mergings = linkage(samples, method='single')

# Plot the dendrogram
dendrogram(mergings, labels=country_names, leaf_rotation=90, leaf_font_size=6)
plt.show()



###Extracting the cluster labels
# Perform the necessary imports
import pandas as pd
from scipy.cluster.hierarchy import fcluster

# Use fcluster to extract labels: labels
labels = fcluster(mergings, 6, criterion='distance')

# Create a DataFrame with labels and varieties as columns: df
df = pd.DataFrame({'labels': labels, 'varieties': varieties})

# Create crosstab: ct
ct = pd.crosstab(df['labels'], df['varieties'])

# Display ct
print(ct)



###t-SNE visualization of grain dataset
# Import TSNE
from sklearn.manifold import TSNE

# Create a TSNE instance: model
model = TSNE(learning_rate=200)

# Apply fit_transform to samples: tsne_features
tsne_features = model.fit_transform(samples)

# Select the 0th feature: xs
xs = tsne_features[:,0]

# Select the 1st feature: ys
ys = tsne_features[:,1]

# Scatter plot, coloring by variety_numbers
plt.scatter(xs, ys, c=variety_numbers)
plt.show()



###A t-SNE map of the stock market
# Import TSNE
from sklearn.manifold import TSNE

# Create a TSNE instance: model
model = TSNE(learning_rate=50)

# Apply fit_transform to normalized_movements: tsne_features
tsne_features = model.fit_transform(normalized_movements)

# Select the 0th feature: xs
xs = tsne_features[:,0]

# Select the 1th feature: ys
ys = tsne_features[:,1]

# Scatter plot
plt.scatter(xs, ys, alpha=0.5)

# Annotate the points
for x, y, company in zip(xs, ys, companies):
    plt.annotate(company, (x, y), fontsize=5, alpha=0.75)
plt.show()