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preprocessing dataset in python

_loganthx answered on May 6, 2023 Popularity 8/10 Helpfulness 1/10

answer preprocessing dataset in python

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preprocessing dataset in python

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'''Preprocessing dataset: (Suppose your .py file is inside Dataset folder
and Labels are folder's names: Example => C:\Users\Dataset\Cats\cat1.png)'''
import os, cv2 as cv, numpy as np, random

DIR = os.getcwd()
Labels = [i for i in os.listdir(DIR) if not('.') in i]
data, Cut, X, y, New_W, New_H = [], [], [], [], 50, 50

for Root, dir, files in os.walk(DIR):
	if Root!=DIR: Cut.append(len(os.listdir(Root))) # Normalize samples
      
for R, d, f in os.walk(DIR):
	if R!= DIR: # Loads img as grayscale, resizing and appending => [img, label]
		lbl = [(Labels[id]) for id, _ in enumerate(Labels) if Labels[id] in R]
		Imgs = [(cv.imread(f'{R}\\{img}', 0)/255.0) for img in os.listdir(R)]
		for img in Imgs[:min(Cut)]: 
			data.append([cv.resize(img, (New_W, New_H)), lbl[0]])
		print(f'Num of imgs for {lbl} label ==> {len(Imgs[:min(Cut)])}')
random.shuffle(data)

for img, label in data: 
	X.append(img)
	y.append(label)
np.array(X).reshape(-1, New_W, New_H, 1)   #1 if gray; 3 if rgb
  
np.save(f"{DIR}\\X.npx", X) # Save file in DIR directory
np.save(f"{DIR}\\y.npx", y)

Popularity 8/10 Helpfulness 1/10 Language python

Source: Grepper

Tags: dataset python

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Contributed on May 06 2023

_loganthx

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# Inspect dataset
df.head()
df.info()
df.describe() # Summary stats

# DEAL WITH MISSING VALUES
df.drop([1, 2, 3]) # Drop specific rows
df.dropna(thresh=2) # keep at least 2 non-missing values in each row
df.dropna(subset=['C']) # Drop missing values of specified column

# Convert column types
df["C"] = df["C"].astype("float")

# Verify class imbalance
y.value_counts()

# Split into training and testing data (Consider class imbalance)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)

# STANDARDIZE DATASET
df.var() # Detect high variance difference in columns are candidates of log normalization

# FEATURE ENGINEERING
# TEXT PROCESSING (cleaning / vectorizing / regular expression )

Popularity 6/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: python

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Contributed on Jan 16 2024

Innocent Iguana

0 Answers Avg Quality 2/10

preprocessing dataset in python

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