#!/usr/bin/env python
# coding: utf-8

# # Selecting features for modeling
# > This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA). This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.
# 
# - toc: true 
# - badges: true
# - comments: true
# - author: Chanseok Kang
# - categories: [Python, Datacamp, Machine_Learning]
# - image: 

# In[1]:


import pandas as pd
import numpy as np


# ## Feature selection
# - Selecting features to be used for modeling
# - Doesn't create new features
# - Improve model's performance

# ### Identifying areas for feature selection
# Take an exploratory look at the post-feature engineering `hiking` dataset. 

# In[2]:


hiking = pd.read_json('./dataset/hiking.json')
hiking.head()


# ## Removing redundant features
# - Remove noisy features
# - Remove correlated features
#     - Statistically correlated: features move together directionally
#     - Linear models assume feature independence
#     - Pearson correlation coefficient
# - Remove duplicated features

# ### Selecting relevant features
# Now let's identify the redundant columns in the `volunteer` dataset and perform feature selection on the dataset to return a DataFrame of the relevant features.
# 
# For example, if you explore the `volunteer` dataset in the console, you'll see three features which are related to location: `locality`, `region`, and `postalcode`. They contain repeated information, so it would make sense to keep only one of the features.
# 
# There are also features that have gone through the feature engineering process: columns like `Education` and `Emergency Preparedness` are a product of encoding the categorical variable `category_desc`, so `category_desc` itself is redundant now.
# 
# Take a moment to examine the features of volunteer in the console, and try to identify the redundant features.

# In[3]:


volunteer = pd.read_csv('./dataset/volunteer_sample.csv')
volunteer.dropna(subset=['category_desc'], axis=0, inplace=True)
volunteer.head()


# In[4]:


volunteer.columns


# In[5]:


# Create a list of redundant column names to drop
to_drop = ["locality", "region", "category_desc", "created_date", "vol_requests"]

# Drop those columns from the dataset
volunteer_subset = volunteer.drop(to_drop, axis=1)

# Print out the head of the new dataset
volunteer_subset.head()


# ### Checking for correlated features
# Let's take a look at the `wine` dataset again, which is made up of continuous, numerical features. Run Pearson's correlation coefficient on the dataset to determine which columns are good candidates for eliminating. Then, remove those columns from the DataFrame.
# 
# 

# In[6]:


wine = pd.read_csv('./dataset/wine_sample.csv')
wine.head()


# In[7]:


# Print out the column correlations of the wine dataset
print(wine.corr())


# In[8]:


# Take a minute to find the column where the correlation value is greater than 0.75 at least twice
to_drop = "Flavanoids"

# Drop that column from the DataFrame
wine = wine.drop(to_drop, axis=1)


# ## Selecting features using text vectors
# 

# ### Exploring text vectors, part 1
# Let's expand on the text vector exploration method we just learned about, using the `volunteer` dataset's title tf/idf vectors. In this first part of text vector exploration, we're going to add to that function we learned about in the slides. We'll return a list of numbers with the function. In the next exercise, we'll write another function to collect the top words across all documents, extract them, and then use that list to filter down our `text_tfidf` vector.

# In[12]:


vocab_csv = pd.read_csv('./dataset/vocab_volunteer.csv', index_col=0).to_dict()
vocab = vocab_csv['0']


# In[13]:


volunteer = volunteer[['category_desc', 'title']]
volunteer = volunteer.dropna(subset=['category_desc'], axis=0)


# In[14]:


from sklearn.feature_extraction.text import TfidfVectorizer

# Take the title text
title_text = volunteer['title']

# Create the vectorizer method
tfidf_vec = TfidfVectorizer()

# Transform the text into tf-idf vectors
text_tfidf = tfidf_vec.fit_transform(title_text)


# In[15]:


# Add in the rest of the parameters
def return_weights(vocab, original_vocab, vector, vector_index, top_n):
    zipped = dict(zip(vector[vector_index].indices, vector[vector_index].data))
    
    # Let's transform that zipped dict into a series
    zipped_series = pd.Series({vocab[i]:zipped[i] for i in vector[vector_index].indices})
    
    # Let's sort the series to pull out the top n weighted words
    zipped_index = zipped_series.sort_values(ascending=False)[:top_n].index
    return [original_vocab[i] for i in zipped_index]

# Print out the weighted words
print(return_weights(vocab, tfidf_vec.vocabulary_, text_tfidf, vector_index=8, top_n=3))


# ### Exploring text vectors, part 2
# Using the function we wrote in the previous exercise, we're going to extract the top words from each document in the text vector, return a list of the word indices, and use that list to filter the text vector down to those top words.

# In[16]:


def words_to_filter(vocab, original_vocab, vector, top_n):
    filter_list = []
    for i in range(0, vector.shape[0]):
        # here we'll call the function from the previous exercise, 
        # and extend the list we're creating
        filtered = return_weights(vocab, original_vocab, vector, i, top_n)
        filter_list.extend(filtered)
    # Return the list in a set, so we don't get duplicate word indices
    return set(filter_list)

# Call the function to get the list of word indices
filtered_words = words_to_filter(vocab, tfidf_vec.vocabulary_, text_tfidf, top_n=3)

# By converting filtered_words back to a list, 
# we can use it to filter the columns in the text vector
filtered_text = text_tfidf[:, list(filtered_words)]


# ### Training Naive Bayes with feature selection
# Let's re-run the Naive Bayes text classification model we ran at the end of chapter 3, with our selection choices from the previous exercise, on the `volunteer` dataset's `title` and `category_desc` columns.
# 
# 

# In[17]:


from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB

nb = GaussianNB()
y = volunteer['category_desc']

# Split the dataset according to the class distribution of category_desc,
# using the filtered_text vector
X_train, X_test, y_train, y_test = train_test_split(filtered_text.toarray(), y, stratify=y)

# Fit the model to the training data
nb.fit(X_train, y_train)

# Print out the model's accuracy
print(nb.score(X_test, y_test))


# You can see that our accuracy score wasn't that different from the score at the end of chapter 3. That's okay; the `title` field is a very small text field, appropriate for demonstrating how filtering vectors works.

# ## Dimensionality reduction
# - Unsupervised learning method
# - Combine/decomposes a feature space
# - Feature extraction
# - Principal component analysis
#     - Linear transformation to uncorrelated space
#     - Captures as much variance as possible in each component
# - PCA caveats
#     - Difficult to interpret components
#     - End of preprocessing journey

# ### Using PCA
# Let's apply PCA to the `wine` dataset, to see if we can get an increase in our model's accuracy.

# In[18]:


wine = pd.read_csv('./dataset/wine_types.csv')
wine.head()


# In[19]:


from sklearn.decomposition import PCA

# Set up PCA and the X vector for dimensionality reduction
pca = PCA()
wine_X = wine.drop('Type', axis=1)

# Apply PCA to the wine dataset X vector
transformed_X = pca.fit_transform(wine_X)

# Look at the percentage of variance explained by the different components
print(pca.explained_variance_ratio_)


# ### Training a model with PCA
# Now that we have run PCA on the `wine` dataset, let's try training a model with it.

# In[21]:


from sklearn.neighbors import KNeighborsClassifier

y = wine['Type']

knn = KNeighborsClassifier()

# Split the transformed X and the y labels into training and test sets
X_wine_train, X_wine_test, y_wine_train, y_wine_test = train_test_split(transformed_X, y)

# Fit knn to the training data
knn.fit(X_wine_train, y_wine_train)

# Score knn on the test data and print it out
print(knn.score(X_wine_test, y_wine_test))