neelindresh

import numpy as np
import cv2
import math
from scipy import ndimage
import matplotlib.pyplot as plt
from lxml import etree 
import xml.etree.cElementTree as xml
import os
from PIL import Image
"""Tools for satellite imagery pre-processing"""

neelindresh

from sklearn.datasets import  load_iris
iris_data=load_iris()
data=iris_data["data"]
target=iris_data["target"]
df=pd.DataFrame(data,columns=iris_data["feature_names"])

neelindresh

from pymlpipe.tabular import PyMLPipe
mlp=PyMLPipe()
mlp.set_experiment("IrisDataV2")
mlp.set_version(0.1)

neelindresh

from sklearn.model_selection import train_test_split
trainx,testx,trainy,testy=train_test_split(df,target)

neelindresh

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import  RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score

neelindresh

with mlp.run():
    mlp.set_tags(["Classification","test run","dtree"])
    model=DecisionTreeClassifier()
    model.fit(trainx, trainy)
    predictions=model.predict(testx)
    
    mlp.log_metrics({"Accuracy":accuracy_score(testy,predictions),"Precision": precision_score(testy,predictions,average='macro')})
    
    mlp.log_metric("Recall", recall_score(testy,predictions,average='macro'))
    mlp.log_metric("F1", f1_score(testy,predictions,average='macro'))

neelindresh

from sklearn.datasets import  load_iris
import pandas as pd
from sklearn.model_selection import train_test_split
from pymlpipe.tabular import PyMLPipe
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import  RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score

neelindresh

{
    "data": [
        [
            5.6,
            3.0,
            4.5,
            1.5
        ],
        [
            5.6,

neelindresh

import torch
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,f1_score
from pymlpipe.tabular import PyMLPipe

neelindresh

df=pd.read_csv("train.csv")
encoders=["area_code","state","international_plan","voice_mail_plan","churn"]

for i in encoders:
    le=LabelEncoder()
    df[i]=le.fit_transform(df[i])
    
    
trainy=df["churn"]
trainx=df[['state', 'account_length', 'area_code', 'international_plan',