James Triveri jtrive84

## RiskMapMultiModal.ipynb

      
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                jtrive84
                / RiskMapMultiModal.ipynb
            
            
              Created
              April 4, 2024 15:23
            
          
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## clf_eval.py
import matplotlib.pyplot as plt
from sklearn.metrics import (
    PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay, roc_curve,
    ConfusionMatrixDisplay, confusion_matrix
    )

beta = 2
prior_thresh = .50

p1, r1, thresh1 = precision_recall_curve(yactual, ypred1)

## clf_metrics.py

import matplotlib.pyplot as plt
from sklearn.metrics import (
    PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay, roc_curve
    )

p1, r1, thresh1 = precision_recall_curve(yactual, ypred1)
p2, r2, thresh2 = precision_recall_curve(yactual, ypred2)

# Determine threshold that maximizes f1 score.

## ames-housing-small.csv

          
            lot_area
            bld_type
            house_style
            exterior
            foundation
            basement_sqft
            central_air
            first_floor_sqft
            full_bath
            kitchen_score
            fireplaces
            garage_type
            garage_nbr_cars
            paved_drive
            sale_price

            
              31770
              1Fam
              1Story
              BrkFace
              CBlock
              1080
              Y
              1656
              1
              TA
              2
              Attchd
              2
              P
              215000

            
              11622
              1Fam
              1Story
              VinylSd
              CBlock
              882
              Y
              896
              1
              TA
              0
              Attchd
              1
              Y
              105000

            
              14267
              1Fam
              1Story
              Wd Sdng
              CBlock
              1329
              Y
              1329
              1
              Gd
              0
              Attchd
              1
              Y
              172000

            
              11160
              1Fam
              1Story
              BrkFace
              CBlock
              2110
              Y
              2110
              2
              Ex
              2
              Attchd
              2
              Y
              244000

            
              13830
              1Fam
              2Story
              VinylSd
              PConc
              928
              Y
              928
              2
              TA
              1
              Attchd
              2
              Y
              189900

            
              9978
              1Fam
              2Story
              VinylSd
              PConc
              926
              Y
              926
              2
              Gd
              1
              Attchd
              2
              Y
              195500

            
              4920
              TwnhsE
              1Story
              CemntBd
              PConc
              1338
              Y
              1338
              2
              Gd
              0
              Attchd
              2
              Y
              213500

            
              5005
              TwnhsE
              1Story
              HdBoard
              PConc
              1280
              Y
              1280
              2
              Gd
              0
              Attchd
              2
              Y
              191500

            
              5389
              TwnhsE
              1Story
              CemntBd
              PConc
              1595
              Y
              1616
              2
              Gd
              1
              Attchd
              2
              Y
              236500

## cnn_binary.py
from PIL import Image
import torch
import torchvision
from torch.utils.data import Dataset
from torchvision import transforms, utils
torchvision.disable_beta_transforms_warning()
from torchvision.transforms import v2


## ec2_setup.sh
#!/usr/bin/env bash
# -----------------------------------------------------------------------------------                                                                              |
# Copy configuration files and setup Python on EC2                                  |
# Author: James D. Triveri                                                          |
# Date: 2022-02                                                                     |
#                                                                                   |
#   ./ec2_setup.sh --host=ec2g --send-configs --run-on-ec2 --sync-s3                |
# -----------------------------------------------------------------------------------

SETUP_DIR="$(cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd)"

## Eigenrankings.ipynb

      
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                jtrive84
                / Eigenrankings.ipynb
            
            
              Last active
              March 14, 2024 01:44
            
              
                Eigenrankings
              
          
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## poly_folium.py
import folium
from folium.plugins import BeautifyIcon, DualMap
from folium.features import DivIcon

f = folium.Figure(width=800, height=700)

m = folium.Map(
    location=[mid_lat, mid_lon],
    scrollWheelZoom=True,
    zoom_start=11,

## dual_folium.py
"""
See: https://python-visualization.github.io/folium/latest/user_guide/plugins/dual_map.html
"""
import folium
from folium.plugins import BeautifyIcon, DualMap
from folium.features import DivIcon


tiles = "openstreetmap" # "cartodbpositron"

## NFL-2023-Standings.csv

          
            team
            wins
            losses
            rank

            
              Ravens
              13
              4
              1

            
              Cowboys
              12
              5
              2

            
              Lions
              12
              5
              3

            
              49ers
              12
              5
              4

            
              Bills
              11
              6
              5

            
              Browns
              11
              6
              6

            
              Chiefs
              11
              6
              7

            
              Dolphins
              11
              6
              8

            
              Eagles
              11
              6
              9
	import matplotlib.pyplot as plt
	from sklearn.metrics import (
	PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay, roc_curve,
	ConfusionMatrixDisplay, confusion_matrix
	)

	beta = 2
	prior_thresh = .50

	p1, r1, thresh1 = precision_recall_curve(yactual, ypred1)
lot_area	bld_type	house_style	exterior	foundation	basement_sqft	central_air	first_floor_sqft	full_bath	kitchen_score	fireplaces	garage_type	garage_nbr_cars	paved_drive	sale_price
31770	1Fam	1Story	BrkFace	CBlock	1080	Y	1656	1	TA	2	Attchd	2	P	215000
11622	1Fam	1Story	VinylSd	CBlock	882	Y	896	1	TA	0	Attchd	1	Y	105000
14267	1Fam	1Story	Wd Sdng	CBlock	1329	Y	1329	1	Gd	0	Attchd	1	Y	172000
11160	1Fam	1Story	BrkFace	CBlock	2110	Y	2110	2	Ex	2	Attchd	2	Y	244000
13830	1Fam	2Story	VinylSd	PConc	928	Y	928	2	TA	1	Attchd	2	Y	189900
9978	1Fam	2Story	VinylSd	PConc	926	Y	926	2	Gd	1	Attchd	2	Y	195500
4920	TwnhsE	1Story	CemntBd	PConc	1338	Y	1338	2	Gd	0	Attchd	2	Y	213500
5005	TwnhsE	1Story	HdBoard	PConc	1280	Y	1280	2	Gd	0	Attchd	2	Y	191500
5389	TwnhsE	1Story	CemntBd	PConc	1595	Y	1616	2	Gd	1	Attchd	2	Y	236500
	from PIL import Image
	import torch
	import torchvision
	from torch.utils.data import Dataset
	from torchvision import transforms, utils
	torchvision.disable_beta_transforms_warning()
	from torchvision.transforms import v2
	#!/usr/bin/env bash
	# ----------------------------------------------------------------------------------- \|
	# Copy configuration files and setup Python on EC2 \|
	# Author: James D. Triveri \|
	# Date: 2022-02 \|
	# \|
	# ./ec2_setup.sh --host=ec2g --send-configs --run-on-ec2 --sync-s3 \|
	# -----------------------------------------------------------------------------------

	SETUP_DIR="$(cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd)"
	import folium
	from folium.plugins import BeautifyIcon, DualMap
	from folium.features import DivIcon

	f = folium.Figure(width=800, height=700)

	m = folium.Map(
	location=[mid_lat, mid_lon],
	scrollWheelZoom=True,
	zoom_start=11,
	"""
	See: https://python-visualization.github.io/folium/latest/user_guide/plugins/dual_map.html
	"""
	import folium
	from folium.plugins import BeautifyIcon, DualMap
	from folium.features import DivIcon


	tiles = "openstreetmap" # "cartodbpositron"
team	wins	losses	rank
Ravens	13	4	1
Cowboys	12	5	2
Lions	12	5	3
49ers	12	5	4
Bills	11	6	5
Browns	11	6	6
Chiefs	11	6	7
Dolphins	11	6	8
Eagles	11	6	9