Unsupervised Learning in Python--Discovering interpretable features.py

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 29 16:33:22 2019

@author: z
"""

###############################################################################Unsupervised Learning in Python
###NMF applied to Wikipedia articles
# Import NMF
from sklearn.decomposition import NMF

# Create an NMF instance: model
model = NMF(n_components=6)

# Fit the model to articles
model.fit(articles)

# Transform the articles: nmf_features
nmf_features = model.transform(articles)

# Print the NMF features
print(nmf_features)



###NMF features of the Wikipedia articles
# Import pandas
import pandas as pd

# Create a pandas DataFrame: df
df = pd.DataFrame(nmf_features, index=titles)

# Print the row for 'Anne Hathaway'
print(df.loc['Anne Hathaway'])

# Print the row for 'Denzel Washington'
print(df.loc['Denzel Washington'])



###NMF learns topics of documents
# Import pandas
import pandas as pd

# Create a DataFrame: components_df
components_df = pd.DataFrame(model.components_, columns=words)

# Print the shape of the DataFrame
print(components_df.shape)

# Select row 3: component
component = components_df.iloc[3,:]

# Print result of nlargest
print(component.nlargest())



###Explore the LED digits dataset
# Import pyplot
from matplotlib import pyplot as plt

# Select the 0th row: digit
digit = samples[0,:]

# Print digit
print(digit)

# Reshape digit to a 13x8 array: bitmap
bitmap = digit.reshape(13,8)
# Print bitmap
print(bitmap)

# Use plt.imshow to display bitmap
plt.imshow(bitmap, cmap='gray', interpolation='nearest')
plt.colorbar()
plt.show()



###NMF learns the parts of images
# Import NMF
from sklearn.decomposition import NMF

# Create an NMF model: model
model = NMF(n_components=7)

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

# Call show_as_image on each component
for component in model.components_:
    show_as_image(component)

# Assign the 0th row of features: digit_features
digit_features = features[0,:]

# Print digit_features
print(digit_features)



###PCA doesn't learn parts
# Import PCA
from sklearn.decomposition import PCA

# Create a PCA instance: model
model = PCA(n_components=7)

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

# Call show_as_image on each component
for component in model.components_:
    show_as_image(component)
    
    

###Which articles are similar to 'Cristiano Ronaldo'?
# Perform the necessary imports
import pandas as pd
from sklearn.preprocessing import normalize

# Normalize the NMF features: norm_features
norm_features = normalize(nmf_features)

# Create a DataFrame: df
df = pd.DataFrame(norm_features, index=titles)

# Select the row corresponding to 'Cristiano Ronaldo': article
article = df.loc['Cristiano Ronaldo']

# Compute the dot products: similarities
similarities = df.dot(article)

# Display those with the largest cosine similarity
print(similarities.nlargest())



###Recommend musical artists part I
# Perform the necessary imports
from sklearn.decomposition import NMF
from sklearn.preprocessing import Normalizer, MaxAbsScaler
from sklearn.pipeline import make_pipeline

# Create a MaxAbsScaler: scaler
scaler = MaxAbsScaler()

# Create an NMF model: nmf
nmf = NMF(n_components=20)

# Create a Normalizer: normalizer
normalizer = Normalizer()

# Create a pipeline: pipeline
pipeline = make_pipeline(scaler, nmf, normalizer)

# Apply fit_transform to artists: norm_features
norm_features = pipeline.fit_transform(artists)



###Recommend musical artists part II
# Import pandas
import pandas as pd

# Create a DataFrame: df
df = pd.DataFrame(norm_features, index=artist_names)

# Select row of 'Bruce Springsteen': artist
artist = df.loc['Bruce Springsteen']

# Compute cosine similarities: similarities
similarities = df.dot(artist)

# Display those with highest cosine similarity
print(similarities.nlargest())