Datavisualization
The package only contains methods to simplify your code, so it doesn’t cover all steps of machine learning process, but only those that save you lines of code.
[1]:
#!pip install smltk==2.2.7
# import os
# import sys
# sys.path.insert(1, os.getcwd() + '/../../../')
# print(os.getcwd())
# print(sys.path)
from smltk.datavisualization import DataVisualization
dv = DataVisualization()
Example with sklearn library - iris dataset
[2]:
from sklearn.datasets import load_iris
data = load_iris()
Datavisualization
[3]:
dv.get_df(data).head(5)
[3]:
| target | target_name | sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|---|---|
| 0 | 0 | setosa | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 0 | setosa | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 0 | setosa | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 0 | setosa | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 0 | setosa | 5.0 | 3.6 | 1.4 | 0.2 |
Preprocessing
[4]:
# splitting
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=5)
Modeling
[5]:
# fitting
from sklearn.linear_model import SGDClassifier
model = SGDClassifier(random_state=3)
_ = model.fit(X_train, y_train)
[6]:
# testing
y_pred = model.predict(X_test)
Datavisualization
[7]:
dv.get_inference_df(data, X_test, y_test, y_pred).head(5)
[7]:
| prediction | target | target_name | sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | versicolor | 5.8 | 2.7 | 3.9 | 1.2 |
| 1 | 2 | 2 | virginica | 6.1 | 2.6 | 5.6 | 1.4 |
| 2 | 2 | 2 | virginica | 5.8 | 2.8 | 5.1 | 2.4 |
| 3 | 0 | 0 | setosa | 4.4 | 3.2 | 1.3 | 0.2 |
| 4 | 2 | 2 | virginica | 7.2 | 3.6 | 6.1 | 2.5 |
Example with torch library - image dataset
[8]:
import requests
from PIL import Image
url = 'https://www.projectinvictus.it/wp-content/uploads/2022/08/junk-food-scaled.jpg'
im = Image.open(requests.get(url, stream=True).raw)
Preprocessing
[9]:
# data preparation
import torchvision.transforms as transforms
transform = transforms.Compose([ transforms.Resize(800), transforms.ToTensor() ])
img = transform(im).unsqueeze(0)
Modeling
[10]:
# load pre-trained model instead to train it
import torch
model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)
model.eval();
Using cache found in /Users/alessandrabilardi/.cache/torch/hub/facebookresearch_detr_main
/Users/alessandrabilardi/.env/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/Users/alessandrabilardi/.env/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
[11]:
# fitting
prediction = model(img)
probability, boxes = dv.get_inference_objects(im, prediction, 0.7)
Datavisualization
[12]:
# pretty view of prediction
dv.get_inference_objects_df(probability, boxes)
[12]:
| class | probability | xmin | ymin | xmax | ymax | |
|---|---|---|---|---|---|---|
| 0 | bowl | 0.91 | 121.079979 | 743.922058 | 698.561523 | 1422.093384 |
| 0 | bowl | 0.92 | 696.310791 | 649.603210 | 1255.190918 | 1054.840454 |
| 0 | donut | 1.00 | 2140.089355 | 469.912140 | 2559.624512 | 966.051208 |
| 0 | donut | 1.00 | 1892.986816 | 816.611267 | 2523.677246 | 1351.426514 |
| 0 | cup | 0.88 | 2440.144287 | -1.122757 | 2559.945801 | 397.894653 |
| 0 | donut | 0.78 | 1279.640137 | 728.656555 | 1816.003174 | 1172.515991 |
| 0 | bowl | 0.99 | 718.509521 | 1025.902588 | 1311.592041 | 1424.917969 |
| 0 | cup | 1.00 | 1913.083984 | 1.166088 | 2337.076904 | 347.949921 |
| 0 | dining table | 0.98 | -0.428162 | 4.530998 | 2559.098877 | 1419.098145 |
| 0 | bowl | 0.72 | 2444.689697 | -1.408806 | 2559.865479 | 396.303345 |
| 0 | cup | 0.98 | 120.385170 | 741.396362 | 703.488281 | 1423.120361 |
| 0 | donut | 0.82 | 1331.866211 | 146.230453 | 1958.745361 | 741.035339 |
| 0 | bowl | 0.80 | 1242.721802 | 728.814392 | 1856.770630 | 1345.256592 |
[13]:
# plot image with objects detected
dv.plot_inference_objects(im, probability, boxes)
