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utils.py
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utils.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.auto import tqdm
features_to_drop = ['instance weight',
'enroll in edu inst last wk',
'member of a labor union',
'reason for unemployment',
'region of previous residence',
'state of previous residence',
'migration prev res in sunbelt',
'family members under 18',
"fill inc questionnaire for veteran's admin"]
categorical_features = ['class of worker',
'detailed industry recode',
'detailed occupation recode',
'education',
'marital status',
'major industry code',
'major occupation code',
'race',
'hispanic origin',
'sex',
'full or part time employment stat',
'tax filer stat',
'detailed household and family stat',
'detailed household summary in household',
'migration code-change in msa',
'migration code-change in reg',
'migration code-move within reg',
'live in this house 1 year ago',
'country of birth father',
'country of birth mother',
'country of birth self',
'citizenship',
'own business or self employed',
'veterans benefits',
'year']
numerical_features = ['age',
'wage per hour',
'capital gains',
'capital losses',
'dividends from stocks',
'num persons worked for employer',
'weeks worked in year']
def reduce_mem_usage(df):
start_mem_usg = df.memory_usage().sum() / 1024 ** 2
print("Memory usage of dataframe: ", start_mem_usg, " MB")
na_list = [] # Keeps track of columns that have missing values filled in.
for col in tqdm(df.columns):
if df[col].dtype != object: # Exclude strings
# make variables for Int, max and min
is_int = False
mx = df[col].max()
mn = df[col].min()
# Integer does not support NA, therefore, NA needs to be filled
if not np.isfinite(df[col]).all():
na_list.append(col)
df[col].fillna(mn - 1, inplace=True)
# test if column can be converted to an integer
asint = df[col].fillna(0).astype(np.int64)
result = (df[col] - asint)
result = result.sum()
if -0.01 < result < 0.01:
is_int = True
# Make Integer/unsigned Integer datatypes
if is_int:
if mn >= 0:
if mx < 255:
df[col] = df[col].astype(np.uint8)
elif mx < 65535:
df[col] = df[col].astype(np.uint16)
elif mx < 4294967295:
df[col] = df[col].astype(np.uint32)
else:
df[col] = df[col].astype(np.uint64)
else:
if mn > np.iinfo(np.int8).min and mx < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif mn > np.iinfo(np.int16).min and mx < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif mn > np.iinfo(np.int32).min and mx < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif mn > np.iinfo(np.int64).min and mx < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
# Make float datatypes 32 bit
else:
df[col] = df[col].astype(np.float32)
# Print final result
print("___MEMORY USAGE AFTER COMPLETION:___")
mem_usg = df.memory_usage().sum() / 1024 ** 2
print(f'Memory usage is: {mem_usg} MB')
print(f'This is {100 * mem_usg / start_mem_usg:.2f}% of the initial size')
return df, na_list
def nans_count(df, axis=0):
nans_number = pd.DataFrame(df.isnull().sum(axis=axis) * 100 / df.shape[axis], columns=['nan_count'])
return nans_number[nans_number['nan_count'] > 0]
def unique_values(df):
for column in tqdm(df.columns):
values = list(df[column].unique())
print(f'{column}: {values}')
def pca_results(full_dataset, pca, show_plot=True):
"""
Create a DataFrame of the PCA results
Includes dimension feature weights and explained variance
Visualizes the PCA results
"""
# Dimension indexing
dimensions = ['Dimension {}'.format(i) for i in range(1, len(pca.components_) + 1)]
# PCA components
components = pd.DataFrame(np.round(pca.components_, 4), columns=full_dataset.keys())
components.index = dimensions
# PCA explained variance
ratios = pca.explained_variance_ratio_.reshape(len(pca.components_), 1)
variance_ratios = pd.DataFrame(np.round(ratios, 4), columns=['Explained Variance Ratio'])
variance_ratios.index = dimensions
# PCA explained cumulative variance
cumsum = pca.explained_variance_ratio_.cumsum().reshape(len(pca.components_), 1)
variance_cumsum = pd.DataFrame(np.round(cumsum, 4), columns=['Explained Cumulative Variance'])
variance_cumsum.index = dimensions
if show_plot:
# Create a bar plot visualization
fig, ax = plt.subplots(figsize=(14, 8))
ax.plot(np.arange(len(variance_cumsum)), variance_cumsum)
ax.set_ylabel("Explained Cumulative Variance")
ax.set_xlabel("Number of Principal Components")
# Return a concatenated DataFrame
return pd.concat([variance_cumsum, variance_ratios, components], axis=1)
# Map weights for the first principal component to corresponding feature names
# and then print the linked values, sorted by weight.
def print_pcs(df, pca, comp, k=5):
components = pd.DataFrame(np.round(pca.components_, 4), columns=df.columns)
pc = components.iloc[comp - 1].sort_values(ascending=False)
print(f'Weights for PC{comp}')
print(f'Top {k} weights')
print(pc.head(k))
print('\n')
print(f'Bottom {k} weights')
print(pc.tail(k))
def class_distribution(df):
plt.figure(figsize=(5, 5))
total = df.shape[0]
ax = sns.countplot(x="income class", data=df)
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x() + p.get_width()/2.,
height + 3,
'{0:.2%}'.format(height/total),
ha="center")
plt.title('Distribution of income classes', fontsize=15)
plt.show()
def clean_dataset(df):
# replace field that contains Not in Universe with NaN
df = df.replace(r'Not in universe\w*?', np.nan, regex=True)
# Drop features
df = df.drop(features_to_drop, axis=1)
# Re-encode features
#df['sex'] = df['sex'].map({'Female': 0, 'Male': 1})
df['income class'] = df['income class'].map({'- 50000.': 0, '50000+.': 1})
#df = df.fillna('Unknown')
#df[categorical_features] = df[categorical_features].astype(str)
# Reduce memory usage
df, _ = reduce_mem_usage(df)
return df