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import matplotlib.pyplot as plt | |
from numpy.random import normal, uniform | |
import pandas as pd | |
import os | |
os.listdir() | |
data = pd.read_csv("DSI.csv") | |
data.head() |
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scores = cross_val_score(LinearRegression(), Xr, yr, cv=397, scoring = "r2") | |
print("Cross-validated scores:", scores) | |
print("Average: ", scores.mean()) | |
print("Variance: ", np.std(scores)) |
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loo = LeaveOneOut() | |
ytests = [] | |
ypreds = [] | |
for train_idx, test_idx in loo.split(Xr): | |
X_train, X_test = X_array[train_idx], X_array[test_idx] #requires arrays | |
y_train, y_test = y_array[train_idx], y_array[test_idx] | |
model = LinearRegression() | |
model.fit(X = X_train, y = y_train) | |
y_pred = model.predict(X_test) |
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mapping_dict ={} | |
for i, img_url in enumerate(image_list[0:10000]): | |
img_name = "img_%05d" % (i,) | |
mapping_dict[img_name] = img_url | |
if (img_url == np.nan) | (str(img_url) == "nan"): | |
continue | |
else: | |
# Uses the creds in ~/.aws/credentials | |
s3_image_filename = img_name |
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# http://docs.aws.amazon.com/rekognition/latest/dg/get-started-exercise.html | |
fileName='img_00001' | |
bucket='bucket_name' | |
client=boto3.client('rekognition') | |
## ^^ we only need to do this code once for the following examples. I include it | |
## re-instated in case you want to check out different pics. | |
response = client.detect_labels(Image={'S3Object':{'Bucket':bucket,'Name':fileName}},MinConfidence=75) | |
response |
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fileName= 'img_00006' | |
bucket='bucket_name' | |
text_in_image = client.detect_text(Image={'S3Object':{'Bucket':bucket,'Name':fileName}}) | |
text_in_image["TextDetections"] |
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fileName= 'img_00012' | |
bucket='bucket_name' | |
celeb_detect = client.recognize_celebrities(Image={'S3Object':{'Bucket':bucket,'Name':fileName}}) | |
celeb_detect["CelebrityFaces"] |
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bucket_name = 'bucket_name' | |
s3 = boto3.resource('s3') | |
bucket = s3.Bucket(bucket_name) | |
images = [img.key for img in bucket.objects.all()] #fetches image names from your S3 bucket | |
client = boto3.client('rekognition') | |
results_wide = [] | |
results_long = [] | |
for img in images: |
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model_data['activity'] = model_data['Activity'].map({'Scuba Diving':'Diving', 'Scuba diving':'Diving','2 boats capsized': 'Disaster','Treading water': 'Swimming', | |
'Air Disaster': 'Disaster','Surfing': 'Surfing', 'Night bathing': 'Swimming', 'Snorkeling': 'Swimming','Measuring sharks': 'Handling', | |
'Swimming': 'Swimming', 'Surfing': 'Surfing', 'Kayaking / Fishing': 'Fishing','Body surfing': 'Surfing', 'Fishing': 'Fishing', | |
'Spearfishing': 'Spear Fishing','Swimming, poaching abalone': 'Diving', 'Wading': 'Shallow Water', 'Canoeing': 'Rowing', | |
'Feeding sharks': 'Handling', 'Paddle boarding':'Rowing','SUP':'Rowing','Body boarding':'Surfing', 'Touching a shark': 'Handling', | |
'Attempting to lasso a shark':'Handling','Surfing ':'Surfing','Tagging sharks':'Handling', 'Kakaying': 'Rowing','Washing hands':'Fishing', | |
'Grabbing shark for a selfie': 'Handling', 'Kayak fishin |
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import os | |
import pandas as pd | |
from time import sleep | |
from selenium import webdriver | |
from selenium.webdriver.common.by import By | |
from selenium.webdriver.support.ui import WebDriverWait | |
from selenium.webdriver.support import expected_conditions as EC | |
from selenium.webdriver.firefox.firefox_binary import FirefoxBinary |
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