vikramsoni2

#!/bin/sh
domain="MY_DOMAIN_NAME"
email="CLOUDFLARE_EMAIL"
global_api_key="GLOBAL_API_KEY"
zone_id="ZONE_ID_FOR_DOMAIN"
ip_file="/var/log/dyndns.log"

ipOld=$(cat $ip_file)
ipNow=$(curl ifconfig.co/)

vikramsoni2

# Cloned from bcm94345wlpagb_p2xx.txt 
NVRAMRev=$Rev: 498373 $
sromrev=11
vendid=0x14e4
devid=0x43ab
manfid=0x2d0
prodid=0x06e4
#macaddr=00:90:4c:c5:12:38
macaddr=b8:27:eb:74:f2:6c
nocrc=1

vikramsoni2

y_true = le.inverse_transform(y_valid)
y_pred = le.inverse_transform(y_valid_pred_lr)

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


data = confusion_matrix(y_true, y_pred)

vikramsoni2

def remove_highly_correlated_features(df = None, ths=0.99):
    print("The data has : {} features ".format(len(list(df))))
    correlations = df.corr()
    mask = np.triu(np.ones_like(correlations, dtype=bool))    
    corr_matrix = correlations.abs()
    # Create a True/False mask and apply it
    mask = np.triu(np.ones_like(corr_matrix, dtype=bool))
    tri_df = corr_matrix.mask(mask)
    # List column names of highly correlated features (r >0.98 )
    to_drop = [c for c in tri_df.columns if any(tri_df[c] > ths)]

vikramsoni2

contractions = { 
"ain't": "am not",
"aren't": "are not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
"couldn't": "could not",
"couldn't've": "could not have",
"didn't": "did not",

vikramsoni2

metrics_names = ['loss', 'accuracy', 'top10_accuracy']

plt.figure(figsize=(14,4))
sns.set_style('whitegrid')
for i in range(len(metrics_names)):
    ax = plt.subplot(1, len(metrics_names), i+1)
    ax.plot(history.history[metrics_names[i]], label="train")
    ax.plot(history.history['val_'+ metrics_names[i]], label="valid")
    ax.title.set_text(metrics_names[i])
    

vikramsoni2

from requests import get
from io import BytesIO
from zipfile import ZipFile
import os
from pathlib import Path


destination = "testproject"
file_url = 'https://github.com/vikramsoni2/aihubcli/archive/refs/heads/main.zip'

vikramsoni2

import logging
from bisect import bisect
from logging import getLogger, Formatter, LogRecord, StreamHandler
from typing import Dict


class LevelFormatter(Formatter):
    def __init__(self, formats: Dict[int, str], **kwargs):
        super().__init__()

vikramsoni2

def top_10_accuracy(preds, train_data, is_higher_better=True):
    y_preds = np.asarray(preds).reshape(-1, len(np.unique(y)))
    y_true = train_data.get_label()
    score =  top_k_accuracy_score(y_true, y_preds, k=10)
    return ('top_10_accuracy', score, True)

vikramsoni2

import numpy as np
import pandas as pd
from matplotlib.path import Path
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sns

from typing import Union, List, Tuple

def parallel_coordinates(df: pd.DataFrame,