Compare commits
7 Commits
Author | SHA1 | Date | |
---|---|---|---|
9d8fc7c82e | |||
6cf924a12b | |||
e79757fb04 | |||
b3a896a632 | |||
9289cb8a82 | |||
88f5323fbf | |||
6cb4c72e4d |
3
.dvc/.gitignore
vendored
Normal file
3
.dvc/.gitignore
vendored
Normal file
@ -0,0 +1,3 @@
|
||||
/config.local
|
||||
/tmp
|
||||
/cache
|
4
.dvc/config
Normal file
4
.dvc/config
Normal file
@ -0,0 +1,4 @@
|
||||
[core]
|
||||
remote = ium_ssh_remote
|
||||
['remote "ium_ssh_remote"']
|
||||
url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl/ium-sftp
|
107
.dvc/plots/confusion.json
Normal file
107
.dvc/plots/confusion.json
Normal file
@ -0,0 +1,107 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"facet": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
},
|
||||
"spec": {
|
||||
"transform": [
|
||||
{
|
||||
"aggregate": [
|
||||
{
|
||||
"op": "count",
|
||||
"as": "xy_count"
|
||||
}
|
||||
],
|
||||
"groupby": [
|
||||
"<DVC_METRIC_Y>",
|
||||
"<DVC_METRIC_X>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"impute": "xy_count",
|
||||
"groupby": [
|
||||
"rev",
|
||||
"<DVC_METRIC_Y>"
|
||||
],
|
||||
"key": "<DVC_METRIC_X>",
|
||||
"value": 0
|
||||
},
|
||||
{
|
||||
"impute": "xy_count",
|
||||
"groupby": [
|
||||
"rev",
|
||||
"<DVC_METRIC_X>"
|
||||
],
|
||||
"key": "<DVC_METRIC_Y>",
|
||||
"value": 0
|
||||
},
|
||||
{
|
||||
"joinaggregate": [
|
||||
{
|
||||
"op": "max",
|
||||
"field": "xy_count",
|
||||
"as": "max_count"
|
||||
}
|
||||
],
|
||||
"groupby": []
|
||||
},
|
||||
{
|
||||
"calculate": "datum.xy_count / datum.max_count",
|
||||
"as": "percent_of_max"
|
||||
}
|
||||
],
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "nominal",
|
||||
"sort": "ascending",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "nominal",
|
||||
"sort": "ascending",
|
||||
"title": "<DVC_METRIC_Y_LABEL>"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": "rect",
|
||||
"width": 300,
|
||||
"height": 300,
|
||||
"encoding": {
|
||||
"color": {
|
||||
"field": "xy_count",
|
||||
"type": "quantitative",
|
||||
"title": "",
|
||||
"scale": {
|
||||
"domainMin": 0,
|
||||
"nice": true
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"mark": "text",
|
||||
"encoding": {
|
||||
"text": {
|
||||
"field": "xy_count",
|
||||
"type": "quantitative"
|
||||
},
|
||||
"color": {
|
||||
"condition": {
|
||||
"test": "datum.percent_of_max > 0.5",
|
||||
"value": "white"
|
||||
},
|
||||
"value": "black"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
112
.dvc/plots/confusion_normalized.json
Normal file
112
.dvc/plots/confusion_normalized.json
Normal file
@ -0,0 +1,112 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"facet": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
},
|
||||
"spec": {
|
||||
"transform": [
|
||||
{
|
||||
"aggregate": [
|
||||
{
|
||||
"op": "count",
|
||||
"as": "xy_count"
|
||||
}
|
||||
],
|
||||
"groupby": [
|
||||
"<DVC_METRIC_Y>",
|
||||
"<DVC_METRIC_X>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"impute": "xy_count",
|
||||
"groupby": [
|
||||
"rev",
|
||||
"<DVC_METRIC_Y>"
|
||||
],
|
||||
"key": "<DVC_METRIC_X>",
|
||||
"value": 0
|
||||
},
|
||||
{
|
||||
"impute": "xy_count",
|
||||
"groupby": [
|
||||
"rev",
|
||||
"<DVC_METRIC_X>"
|
||||
],
|
||||
"key": "<DVC_METRIC_Y>",
|
||||
"value": 0
|
||||
},
|
||||
{
|
||||
"joinaggregate": [
|
||||
{
|
||||
"op": "sum",
|
||||
"field": "xy_count",
|
||||
"as": "sum_y"
|
||||
}
|
||||
],
|
||||
"groupby": [
|
||||
"<DVC_METRIC_Y>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"calculate": "datum.xy_count / datum.sum_y",
|
||||
"as": "percent_of_y"
|
||||
}
|
||||
],
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "nominal",
|
||||
"sort": "ascending",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "nominal",
|
||||
"sort": "ascending",
|
||||
"title": "<DVC_METRIC_Y_LABEL>"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": "rect",
|
||||
"width": 300,
|
||||
"height": 300,
|
||||
"encoding": {
|
||||
"color": {
|
||||
"field": "percent_of_y",
|
||||
"type": "quantitative",
|
||||
"title": "",
|
||||
"scale": {
|
||||
"domain": [
|
||||
0,
|
||||
1
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"mark": "text",
|
||||
"encoding": {
|
||||
"text": {
|
||||
"field": "percent_of_y",
|
||||
"type": "quantitative",
|
||||
"format": ".2f"
|
||||
},
|
||||
"color": {
|
||||
"condition": {
|
||||
"test": "datum.percent_of_y > 0.5",
|
||||
"value": "white"
|
||||
},
|
||||
"value": "black"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
31
.dvc/plots/default.json
Normal file
31
.dvc/plots/default.json
Normal file
@ -0,0 +1,31 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"width": 300,
|
||||
"height": 300,
|
||||
"mark": {
|
||||
"type": "line"
|
||||
},
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_Y_LABEL>",
|
||||
"scale": {
|
||||
"zero": false
|
||||
}
|
||||
},
|
||||
"color": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
}
|
||||
}
|
||||
}
|
116
.dvc/plots/linear.json
Normal file
116
.dvc/plots/linear.json
Normal file
@ -0,0 +1,116 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"width": 300,
|
||||
"height": 300,
|
||||
"layer": [
|
||||
{
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_Y_LABEL>",
|
||||
"scale": {
|
||||
"zero": false
|
||||
}
|
||||
},
|
||||
"color": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": "line"
|
||||
},
|
||||
{
|
||||
"selection": {
|
||||
"label": {
|
||||
"type": "single",
|
||||
"nearest": true,
|
||||
"on": "mouseover",
|
||||
"encodings": [
|
||||
"x"
|
||||
],
|
||||
"empty": "none",
|
||||
"clear": "mouseout"
|
||||
}
|
||||
},
|
||||
"mark": "point",
|
||||
"encoding": {
|
||||
"opacity": {
|
||||
"condition": {
|
||||
"selection": "label",
|
||||
"value": 1
|
||||
},
|
||||
"value": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"transform": [
|
||||
{
|
||||
"filter": {
|
||||
"selection": "label"
|
||||
}
|
||||
}
|
||||
],
|
||||
"layer": [
|
||||
{
|
||||
"mark": {
|
||||
"type": "rule",
|
||||
"color": "gray"
|
||||
},
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"encoding": {
|
||||
"text": {
|
||||
"type": "quantitative",
|
||||
"field": "<DVC_METRIC_Y>"
|
||||
},
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": {
|
||||
"type": "text",
|
||||
"align": "left",
|
||||
"dx": 5,
|
||||
"dy": -5
|
||||
},
|
||||
"encoding": {
|
||||
"color": {
|
||||
"type": "nominal",
|
||||
"field": "rev"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
104
.dvc/plots/scatter.json
Normal file
104
.dvc/plots/scatter.json
Normal file
@ -0,0 +1,104 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"width": 300,
|
||||
"height": 300,
|
||||
"layer": [
|
||||
{
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_Y_LABEL>",
|
||||
"scale": {
|
||||
"zero": false
|
||||
}
|
||||
},
|
||||
"color": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": "point"
|
||||
},
|
||||
{
|
||||
"selection": {
|
||||
"label": {
|
||||
"type": "single",
|
||||
"nearest": true,
|
||||
"on": "mouseover",
|
||||
"encodings": [
|
||||
"x"
|
||||
],
|
||||
"empty": "none",
|
||||
"clear": "mouseout"
|
||||
}
|
||||
},
|
||||
"mark": "point",
|
||||
"encoding": {
|
||||
"opacity": {
|
||||
"condition": {
|
||||
"selection": "label",
|
||||
"value": 1
|
||||
},
|
||||
"value": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"transform": [
|
||||
{
|
||||
"filter": {
|
||||
"selection": "label"
|
||||
}
|
||||
}
|
||||
],
|
||||
"layer": [
|
||||
{
|
||||
"encoding": {
|
||||
"text": {
|
||||
"type": "quantitative",
|
||||
"field": "<DVC_METRIC_Y>"
|
||||
},
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative"
|
||||
}
|
||||
},
|
||||
"layer": [
|
||||
{
|
||||
"mark": {
|
||||
"type": "text",
|
||||
"align": "left",
|
||||
"dx": 5,
|
||||
"dy": -5
|
||||
},
|
||||
"encoding": {
|
||||
"color": {
|
||||
"type": "nominal",
|
||||
"field": "rev"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
39
.dvc/plots/smooth.json
Normal file
39
.dvc/plots/smooth.json
Normal file
@ -0,0 +1,39 @@
|
||||
{
|
||||
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
|
||||
"data": {
|
||||
"values": "<DVC_METRIC_DATA>"
|
||||
},
|
||||
"title": "<DVC_METRIC_TITLE>",
|
||||
"mark": {
|
||||
"type": "line"
|
||||
},
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "<DVC_METRIC_X>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_X_LABEL>"
|
||||
},
|
||||
"y": {
|
||||
"field": "<DVC_METRIC_Y>",
|
||||
"type": "quantitative",
|
||||
"title": "<DVC_METRIC_Y_LABEL>",
|
||||
"scale": {
|
||||
"zero": false
|
||||
}
|
||||
},
|
||||
"color": {
|
||||
"field": "rev",
|
||||
"type": "nominal"
|
||||
}
|
||||
},
|
||||
"transform": [
|
||||
{
|
||||
"loess": "<DVC_METRIC_Y>",
|
||||
"on": "<DVC_METRIC_X>",
|
||||
"groupby": [
|
||||
"rev"
|
||||
],
|
||||
"bandwidth": 0.3
|
||||
}
|
||||
]
|
||||
}
|
3
.dvcignore
Normal file
3
.dvcignore
Normal file
@ -0,0 +1,3 @@
|
||||
# Add patterns of files dvc should ignore, which could improve
|
||||
# the performance. Learn more at
|
||||
# https://dvc.org/doc/user-guide/dvcignore
|
1
.gitignore
vendored
Normal file
1
.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
||||
/healthcare-dataset-stroke-data.csv
|
@ -1,11 +1,14 @@
|
||||
FROM ubuntu:latest
|
||||
FROM ubuntu:20.04
|
||||
|
||||
RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
|
||||
RUN pip3 install torch torchvision torchaudio
|
||||
RUN pip3 install sacred && pip3 install GitPython && pip3 install pymongo
|
||||
RUN pip3 install dvc
|
||||
RUN pip3 install 'dvc[ssh]' paramiko
|
||||
WORKDIR /app
|
||||
|
||||
COPY ./create.py ./
|
||||
COPY ./stats.py ./
|
||||
COPY ./stroke-pytorch.py ./
|
||||
COPY ./stroke-pytorch-eval.py ./
|
||||
COPY ./train-dvc.py ./
|
||||
|
26
Jenkinsfile-dvc
Normal file
26
Jenkinsfile-dvc
Normal file
@ -0,0 +1,26 @@
|
||||
pipeline {
|
||||
agent {
|
||||
dockerfile true
|
||||
}
|
||||
parameters{
|
||||
buildSelector(
|
||||
defaultSelector: lastSuccessful(),
|
||||
description: 'Which build to use for copying artifacts',
|
||||
name: 'WHICH_BUILD'
|
||||
)
|
||||
}
|
||||
stages {
|
||||
stage('dvc') {
|
||||
steps {
|
||||
withCredentials([sshUserPrivateKey(credentialsId: '48ac7004-216e-4260-abba-1fe5db753e18', keyFileVariable: 'IUM_SFTP_KEY')]) {
|
||||
copyArtifacts fingerprintArtifacts: true, projectName: 's434766-create-dataset', selector: buildParameter('WHICH_BUILD')
|
||||
sh 'dvc remote add -f -d ium_ssh_remote ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl/ium-sftp'
|
||||
sh 'dvc remote modify --local ium_ssh_remote keyfile $IUM_SFTP_KEY'
|
||||
sh "dvc pull -f"
|
||||
sh "dvc reproduce"
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
982
Y_pred.txt
Normal file
982
Y_pred.txt
Normal file
@ -0,0 +1,982 @@
|
||||
Y_pred: tensor([[0.4872],
|
||||
[0.4938],
|
||||
[0.4877],
|
||||
[0.4895],
|
||||
[0.5006],
|
||||
[0.5167],
|
||||
[0.4903],
|
||||
[0.4989],
|
||||
[0.4862],
|
||||
[0.4968],
|
||||
[0.4934],
|
||||
[0.5040],
|
||||
[0.4892],
|
||||
[0.4974],
|
||||
[0.4890],
|
||||
[0.4721],
|
||||
[0.5116],
|
||||
[0.5110],
|
||||
[0.4855],
|
||||
[0.4984],
|
||||
[0.5017],
|
||||
[0.5006],
|
||||
[0.4886],
|
||||
[0.4913],
|
||||
[0.4301],
|
||||
[0.4860],
|
||||
[0.4787],
|
||||
[0.4398],
|
||||
[0.4972],
|
||||
[0.5086],
|
||||
[0.4883],
|
||||
[0.4826],
|
||||
[0.4974],
|
||||
[0.5033],
|
||||
[0.5028],
|
||||
[0.5021],
|
||||
[0.4950],
|
||||
[0.4961],
|
||||
[0.4947],
|
||||
[0.4904],
|
||||
[0.5038],
|
||||
[0.5164],
|
||||
[0.5000],
|
||||
[0.4857],
|
||||
[0.5055],
|
||||
[0.5123],
|
||||
[0.4929],
|
||||
[0.4955],
|
||||
[0.5039],
|
||||
[0.5038],
|
||||
[0.5066],
|
||||
[0.4943],
|
||||
[0.4964],
|
||||
[0.4909],
|
||||
[0.4354],
|
||||
[0.4985],
|
||||
[0.4993],
|
||||
[0.4937],
|
||||
[0.5037],
|
||||
[0.5025],
|
||||
[0.4902],
|
||||
[0.5077],
|
||||
[0.5038],
|
||||
[0.4854],
|
||||
[0.4910],
|
||||
[0.5031],
|
||||
[0.4944],
|
||||
[0.4974],
|
||||
[0.4846],
|
||||
[0.5012],
|
||||
[0.4508],
|
||||
[0.4872],
|
||||
[0.4736],
|
||||
[0.4960],
|
||||
[0.4873],
|
||||
[0.4955],
|
||||
[0.4868],
|
||||
[0.5062],
|
||||
[0.5045],
|
||||
[0.4948],
|
||||
[0.4632],
|
||||
[0.4841],
|
||||
[0.4848],
|
||||
[0.5015],
|
||||
[0.5026],
|
||||
[0.5046],
|
||||
[0.4894],
|
||||
[0.5026],
|
||||
[0.4958],
|
||||
[0.4698],
|
||||
[0.4351],
|
||||
[0.5006],
|
||||
[0.4992],
|
||||
[0.5165],
|
||||
[0.4891],
|
||||
[0.4919],
|
||||
[0.4790],
|
||||
[0.4921],
|
||||
[0.4319],
|
||||
[0.4898],
|
||||
[0.4970],
|
||||
[0.4979],
|
||||
[0.4629],
|
||||
[0.5086],
|
||||
[0.4978],
|
||||
[0.5005],
|
||||
[0.5020],
|
||||
[0.3989],
|
||||
[0.4747],
|
||||
[0.4864],
|
||||
[0.4721],
|
||||
[0.4868],
|
||||
[0.5021],
|
||||
[0.4906],
|
||||
[0.5032],
|
||||
[0.4737],
|
||||
[0.5086],
|
||||
[0.4896],
|
||||
[0.5052],
|
||||
[0.4956],
|
||||
[0.4923],
|
||||
[0.4875],
|
||||
[0.4935],
|
||||
[0.4828],
|
||||
[0.4888],
|
||||
[0.5130],
|
||||
[0.5018],
|
||||
[0.5004],
|
||||
[0.4897],
|
||||
[0.4762],
|
||||
[0.4922],
|
||||
[0.4976],
|
||||
[0.4872],
|
||||
[0.4411],
|
||||
[0.4908],
|
||||
[0.4802],
|
||||
[0.4968],
|
||||
[0.4756],
|
||||
[0.5011],
|
||||
[0.5062],
|
||||
[0.4966],
|
||||
[0.5038],
|
||||
[0.5107],
|
||||
[0.4988],
|
||||
[0.4916],
|
||||
[0.4901],
|
||||
[0.4996],
|
||||
[0.3924],
|
||||
[0.4911],
|
||||
[0.4887],
|
||||
[0.4813],
|
||||
[0.4944],
|
||||
[0.4993],
|
||||
[0.5113],
|
||||
[0.5011],
|
||||
[0.4351],
|
||||
[0.4944],
|
||||
[0.4764],
|
||||
[0.5010],
|
||||
[0.4346],
|
||||
[0.5033],
|
||||
[0.5041],
|
||||
[0.4993],
|
||||
[0.4990],
|
||||
[0.4131],
|
||||
[0.4944],
|
||||
[0.4974],
|
||||
[0.5033],
|
||||
[0.4298],
|
||||
[0.4962],
|
||||
[0.5045],
|
||||
[0.5092],
|
||||
[0.4911],
|
||||
[0.4825],
|
||||
[0.4972],
|
||||
[0.5082],
|
||||
[0.5002],
|
||||
[0.5032],
|
||||
[0.4936],
|
||||
[0.4906],
|
||||
[0.4910],
|
||||
[0.5043],
|
||||
[0.4834],
|
||||
[0.5135],
|
||||
[0.5071],
|
||||
[0.4916],
|
||||
[0.4912],
|
||||
[0.4942],
|
||||
[0.4891],
|
||||
[0.4893],
|
||||
[0.5071],
|
||||
[0.4944],
|
||||
[0.4947],
|
||||
[0.5013],
|
||||
[0.4990],
|
||||
[0.4847],
|
||||
[0.4421],
|
||||
[0.4944],
|
||||
[0.4870],
|
||||
[0.4959],
|
||||
[0.4900],
|
||||
[0.4885],
|
||||
[0.4995],
|
||||
[0.5012],
|
||||
[0.5046],
|
||||
[0.4920],
|
||||
[0.5066],
|
||||
[0.5061],
|
||||
[0.4937],
|
||||
[0.5096],
|
||||
[0.5002],
|
||||
[0.5048],
|
||||
[0.4965],
|
||||
[0.4931],
|
||||
[0.5007],
|
||||
[0.4974],
|
||||
[0.4936],
|
||||
[0.4347],
|
||||
[0.4920],
|
||||
[0.4923],
|
||||
[0.4933],
|
||||
[0.5055],
|
||||
[0.5101],
|
||||
[0.5105],
|
||||
[0.4882],
|
||||
[0.4920],
|
||||
[0.5000],
|
||||
[0.5063],
|
||||
[0.5091],
|
||||
[0.4454],
|
||||
[0.5023],
|
||||
[0.4797],
|
||||
[0.4843],
|
||||
[0.4631],
|
||||
[0.5003],
|
||||
[0.5003],
|
||||
[0.5123],
|
||||
[0.5067],
|
||||
[0.5102],
|
||||
[0.5025],
|
||||
[0.4390],
|
||||
[0.5011],
|
||||
[0.4910],
|
||||
[0.4944],
|
||||
[0.4934],
|
||||
[0.5062],
|
||||
[0.4872],
|
||||
[0.5042],
|
||||
[0.4882],
|
||||
[0.4962],
|
||||
[0.5008],
|
||||
[0.5036],
|
||||
[0.5099],
|
||||
[0.4992],
|
||||
[0.4408],
|
||||
[0.4892],
|
||||
[0.5056],
|
||||
[0.4999],
|
||||
[0.4957],
|
||||
[0.4735],
|
||||
[0.4477],
|
||||
[0.5073],
|
||||
[0.4896],
|
||||
[0.4925],
|
||||
[0.4764],
|
||||
[0.4813],
|
||||
[0.4879],
|
||||
[0.4959],
|
||||
[0.4381],
|
||||
[0.4815],
|
||||
[0.5020],
|
||||
[0.4859],
|
||||
[0.4770],
|
||||
[0.4488],
|
||||
[0.4985],
|
||||
[0.4804],
|
||||
[0.4872],
|
||||
[0.4952],
|
||||
[0.4930],
|
||||
[0.5021],
|
||||
[0.4271],
|
||||
[0.5039],
|
||||
[0.4982],
|
||||
[0.4883],
|
||||
[0.4766],
|
||||
[0.5056],
|
||||
[0.4975],
|
||||
[0.5068],
|
||||
[0.5043],
|
||||
[0.4994],
|
||||
[0.4992],
|
||||
[0.4894],
|
||||
[0.5166],
|
||||
[0.4983],
|
||||
[0.5047],
|
||||
[0.4831],
|
||||
[0.4886],
|
||||
[0.5022],
|
||||
[0.4901],
|
||||
[0.4905],
|
||||
[0.4329],
|
||||
[0.5003],
|
||||
[0.5014],
|
||||
[0.5095],
|
||||
[0.4297],
|
||||
[0.4918],
|
||||
[0.4942],
|
||||
[0.4238],
|
||||
[0.4922],
|
||||
[0.4974],
|
||||
[0.5018],
|
||||
[0.4532],
|
||||
[0.4854],
|
||||
[0.5050],
|
||||
[0.4988],
|
||||
[0.4340],
|
||||
[0.4922],
|
||||
[0.4533],
|
||||
[0.4923],
|
||||
[0.4900],
|
||||
[0.4823],
|
||||
[0.4891],
|
||||
[0.5104],
|
||||
[0.4910],
|
||||
[0.4300],
|
||||
[0.5033],
|
||||
[0.4773],
|
||||
[0.5044],
|
||||
[0.4814],
|
||||
[0.5039],
|
||||
[0.5049],
|
||||
[0.4696],
|
||||
[0.4952],
|
||||
[0.5138],
|
||||
[0.5041],
|
||||
[0.5091],
|
||||
[0.5076],
|
||||
[0.4949],
|
||||
[0.5079],
|
||||
[0.4939],
|
||||
[0.4907],
|
||||
[0.5048],
|
||||
[0.4964],
|
||||
[0.4703],
|
||||
[0.5064],
|
||||
[0.4864],
|
||||
[0.4845],
|
||||
[0.4940],
|
||||
[0.4826],
|
||||
[0.5042],
|
||||
[0.5127],
|
||||
[0.5000],
|
||||
[0.4898],
|
||||
[0.4375],
|
||||
[0.5010],
|
||||
[0.5025],
|
||||
[0.4911],
|
||||
[0.4805],
|
||||
[0.4959],
|
||||
[0.5036],
|
||||
[0.5043],
|
||||
[0.5007],
|
||||
[0.4946],
|
||||
[0.5015],
|
||||
[0.4927],
|
||||
[0.4880],
|
||||
[0.4929],
|
||||
[0.4915],
|
||||
[0.5050],
|
||||
[0.4927],
|
||||
[0.4916],
|
||||
[0.4354],
|
||||
[0.4890],
|
||||
[0.5050],
|
||||
[0.5043],
|
||||
[0.4868],
|
||||
[0.4676],
|
||||
[0.4903],
|
||||
[0.5005],
|
||||
[0.4925],
|
||||
[0.4997],
|
||||
[0.4921],
|
||||
[0.4222],
|
||||
[0.4876],
|
||||
[0.5039],
|
||||
[0.5001],
|
||||
[0.4909],
|
||||
[0.4957],
|
||||
[0.4844],
|
||||
[0.4976],
|
||||
[0.5078],
|
||||
[0.4994],
|
||||
[0.5084],
|
||||
[0.4306],
|
||||
[0.5001],
|
||||
[0.4990],
|
||||
[0.4967],
|
||||
[0.4147],
|
||||
[0.5024],
|
||||
[0.4912],
|
||||
[0.4878],
|
||||
[0.5004],
|
||||
[0.5028],
|
||||
[0.4869],
|
||||
[0.4869],
|
||||
[0.4877],
|
||||
[0.4822],
|
||||
[0.4962],
|
||||
[0.4940],
|
||||
[0.4899],
|
||||
[0.5031],
|
||||
[0.4289],
|
||||
[0.4560],
|
||||
[0.5159],
|
||||
[0.5012],
|
||||
[0.5126],
|
||||
[0.4717],
|
||||
[0.5161],
|
||||
[0.4866],
|
||||
[0.5133],
|
||||
[0.4863],
|
||||
[0.4323],
|
||||
[0.5007],
|
||||
[0.4746],
|
||||
[0.4967],
|
||||
[0.4946],
|
||||
[0.5289],
|
||||
[0.5059],
|
||||
[0.4881],
|
||||
[0.4995],
|
||||
[0.4721],
|
||||
[0.4892],
|
||||
[0.5034],
|
||||
[0.4837],
|
||||
[0.5018],
|
||||
[0.5079],
|
||||
[0.4882],
|
||||
[0.4932],
|
||||
[0.5081],
|
||||
[0.4904],
|
||||
[0.4843],
|
||||
[0.4840],
|
||||
[0.5017],
|
||||
[0.4841],
|
||||
[0.4268],
|
||||
[0.4986],
|
||||
[0.4455],
|
||||
[0.5070],
|
||||
[0.5049],
|
||||
[0.4907],
|
||||
[0.4372],
|
||||
[0.5046],
|
||||
[0.4870],
|
||||
[0.5077],
|
||||
[0.4940],
|
||||
[0.4403],
|
||||
[0.4724],
|
||||
[0.5072],
|
||||
[0.4974],
|
||||
[0.5007],
|
||||
[0.4853],
|
||||
[0.4860],
|
||||
[0.4879],
|
||||
[0.4836],
|
||||
[0.5113],
|
||||
[0.4929],
|
||||
[0.4422],
|
||||
[0.5051],
|
||||
[0.4912],
|
||||
[0.4975],
|
||||
[0.5136],
|
||||
[0.4788],
|
||||
[0.4323],
|
||||
[0.5086],
|
||||
[0.4922],
|
||||
[0.4774],
|
||||
[0.4936],
|
||||
[0.5021],
|
||||
[0.5040],
|
||||
[0.4588],
|
||||
[0.4922],
|
||||
[0.5064],
|
||||
[0.5094],
|
||||
[0.4449],
|
||||
[0.5122],
|
||||
[0.4799],
|
||||
[0.4901],
|
||||
[0.5018],
|
||||
[0.5057],
|
||||
[0.4368],
|
||||
[0.5068],
|
||||
[0.5033],
|
||||
[0.5028],
|
||||
[0.4181],
|
||||
[0.4981],
|
||||
[0.5016],
|
||||
[0.5025],
|
||||
[0.5029],
|
||||
[0.4781],
|
||||
[0.5169],
|
||||
[0.4894],
|
||||
[0.4985],
|
||||
[0.5041],
|
||||
[0.4280],
|
||||
[0.4982],
|
||||
[0.4816],
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[0.4653],
|
||||
[0.5024],
|
||||
[0.5018],
|
||||
[0.4760],
|
||||
[0.4912],
|
||||
[0.5014],
|
||||
[0.4876],
|
||||
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|
||||
[0.4877],
|
||||
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|
||||
[0.5024],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[0.5019],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[0.4901],
|
||||
[0.5002],
|
||||
[0.4497],
|
||||
[0.4983],
|
||||
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|
||||
[0.4791],
|
||||
[0.4962],
|
||||
[0.4764],
|
||||
[0.4692],
|
||||
[0.4826],
|
||||
[0.4771],
|
||||
[0.5020],
|
||||
[0.5032],
|
||||
[0.4891],
|
||||
[0.4974],
|
||||
[0.4965],
|
||||
[0.4890],
|
||||
[0.5011],
|
||||
[0.4928],
|
||||
[0.4996],
|
||||
[0.4761],
|
||||
[0.4941],
|
||||
[0.4861],
|
||||
[0.4993],
|
||||
[0.4928],
|
||||
[0.4972],
|
||||
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|
||||
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|
||||
[0.5020],
|
||||
[0.4945],
|
||||
[0.4872],
|
||||
[0.5054],
|
||||
[0.4970],
|
||||
[0.5066],
|
||||
[0.5125],
|
||||
[0.4384],
|
||||
[0.4860],
|
||||
[0.4864],
|
||||
[0.4434],
|
||||
[0.5045],
|
||||
[0.4907],
|
||||
[0.4956],
|
||||
[0.5060],
|
||||
[0.4958],
|
||||
[0.4968],
|
||||
[0.4959],
|
||||
[0.4996],
|
||||
[0.4906],
|
||||
[0.4782],
|
||||
[0.5001],
|
||||
[0.4416],
|
||||
[0.5139],
|
||||
[0.5059],
|
||||
[0.5049],
|
||||
[0.5051],
|
||||
[0.4932],
|
||||
[0.4943],
|
||||
[0.4803],
|
||||
[0.4954],
|
||||
[0.5076],
|
||||
[0.5052],
|
||||
[0.4973],
|
||||
[0.5015],
|
||||
[0.4621],
|
||||
[0.4859],
|
||||
[0.4907],
|
||||
[0.5013],
|
||||
[0.4128],
|
||||
[0.5062],
|
||||
[0.4383],
|
||||
[0.5157],
|
||||
[0.4771],
|
||||
[0.4864],
|
||||
[0.5069],
|
||||
[0.5040],
|
||||
[0.5051],
|
||||
[0.5032],
|
||||
[0.5010],
|
||||
[0.4858],
|
||||
[0.4966],
|
||||
[0.4887],
|
||||
[0.4948],
|
||||
[0.4991],
|
||||
[0.4954],
|
||||
[0.4732],
|
||||
[0.4883],
|
||||
[0.4954],
|
||||
[0.4775],
|
||||
[0.4760],
|
||||
[0.4915],
|
||||
[0.4932],
|
||||
[0.4852],
|
||||
[0.4955],
|
||||
[0.4788],
|
||||
[0.4908],
|
||||
[0.5184],
|
||||
[0.4984],
|
||||
[0.4843],
|
||||
[0.5047],
|
||||
[0.4831],
|
||||
[0.4982],
|
||||
[0.4781],
|
||||
[0.4960],
|
||||
[0.4760],
|
||||
[0.4909]])
|
@ -39,7 +39,7 @@ def saveToCSV(data1,data2,data3):
|
||||
|
||||
|
||||
|
||||
downloadCSV()
|
||||
# downloadCSV()
|
||||
data = dropNaN()
|
||||
data = NormalizeData(data)
|
||||
|
||||
|
19
dvc.yaml
Normal file
19
dvc.yaml
Normal file
@ -0,0 +1,19 @@
|
||||
stages:
|
||||
download_and_split:
|
||||
cmd: python3 create.py
|
||||
deps:
|
||||
- healthcare-dataset-stroke-data.csv
|
||||
- create.py
|
||||
outs:
|
||||
- data_train.csv
|
||||
- data_test.csv
|
||||
- data_val.csv
|
||||
train_model:
|
||||
cmd: python3 train-dvc.py
|
||||
deps:
|
||||
- data_train.csv
|
||||
- data_test.csv
|
||||
- data_val.csv
|
||||
- train-dvc.py
|
||||
outs:
|
||||
- Y_pred.txt
|
File diff suppressed because it is too large
Load Diff
4
healthcare-dataset-stroke-data.csv.dvc
Normal file
4
healthcare-dataset-stroke-data.csv.dvc
Normal file
@ -0,0 +1,4 @@
|
||||
outs:
|
||||
- md5: 3f25a4c2f2963d969e1a156c08968577
|
||||
size: 316971
|
||||
path: healthcare-dataset-stroke-data.csv
|
32
lab2.ipynb
32
lab2.ipynb
@ -10,13 +10,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.5-final"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"orig_nbformat": 2,
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3",
|
||||
"language": "python"
|
||||
"name": "python385jvsc74a57bd02cef13873963874fd5439bd04a135498d1dd9725d9d90f40de0b76178a8e03b1",
|
||||
"display_name": "Python 3.8.5 64-bit ('base': conda)"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
@ -24,7 +23,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -84,7 +83,7 @@
|
||||
" print(data.describe(include='all'))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"downloadCSV()\n",
|
||||
"# downloadCSV()\n",
|
||||
"data = dropNaN()\n",
|
||||
"data = NormalizeData(data)\n",
|
||||
"\n",
|
||||
@ -95,6 +94,27 @@
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['private', 'self_employed', 'govt_job', 'children', 'never_worked'],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.unique(data['work_type'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
|
15
mlruns/0/119a232b4f5b4792afb6fbda18d95262/meta.yaml
Normal file
15
mlruns/0/119a232b4f5b4792afb6fbda18d95262/meta.yaml
Normal file
@ -0,0 +1,15 @@
|
||||
artifact_uri: file:///home/przemek/ium_434766/mlruns/0/119a232b4f5b4792afb6fbda18d95262/artifacts
|
||||
end_time: 1622122401430
|
||||
entry_point_name: ''
|
||||
experiment_id: '0'
|
||||
lifecycle_stage: active
|
||||
name: ''
|
||||
run_id: 119a232b4f5b4792afb6fbda18d95262
|
||||
run_uuid: 119a232b4f5b4792afb6fbda18d95262
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1622122401247
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: owcap
|
1
mlruns/0/119a232b4f5b4792afb6fbda18d95262/metrics/rmse
Normal file
1
mlruns/0/119a232b4f5b4792afb6fbda18d95262/metrics/rmse
Normal file
@ -0,0 +1 @@
|
||||
1622122401410 0.12816519 0
|
@ -0,0 +1 @@
|
||||
0.46289125084877014
|
@ -0,0 +1 @@
|
||||
16
|
1
mlruns/0/119a232b4f5b4792afb6fbda18d95262/params/epochs
Normal file
1
mlruns/0/119a232b4f5b4792afb6fbda18d95262/params/epochs
Normal file
@ -0,0 +1 @@
|
||||
5
|
@ -0,0 +1 @@
|
||||
d4912c0bdcc4ecba96dfd2b643b5e816d51c6bda
|
@ -0,0 +1 @@
|
||||
.\lab8-mlflow.py
|
@ -0,0 +1 @@
|
||||
LOCAL
|
@ -0,0 +1 @@
|
||||
owcap
|
75
train-dvc.py
Normal file
75
train-dvc.py
Normal file
@ -0,0 +1,75 @@
|
||||
import torch
|
||||
import sys
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
from torch.autograd import Variable
|
||||
import torchvision.transforms as transforms
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
from sklearn.metrics import accuracy_score
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sacred import Experiment
|
||||
from sacred.observers import FileStorageObserver
|
||||
np.set_printoptions(suppress=False)
|
||||
|
||||
|
||||
class LogisticRegressionModel(nn.Module):
|
||||
def __init__(self, input_dim, output_dim):
|
||||
super(LogisticRegressionModel, self).__init__()
|
||||
self.linear = nn.Linear(input_dim, output_dim)
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
def forward(self, x):
|
||||
out = self.linear(x)
|
||||
return self.sigmoid(out)
|
||||
|
||||
|
||||
data_train = pd.read_csv("data_train.csv")
|
||||
data_test = pd.read_csv("data_test.csv")
|
||||
data_val = pd.read_csv("data_val.csv")
|
||||
FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
|
||||
|
||||
x_train = data_train[FEATURES].astype(np.float32)
|
||||
y_train = data_train['stroke'].astype(np.float32)
|
||||
|
||||
x_test = data_test[FEATURES].astype(np.float32)
|
||||
y_test = data_test['stroke'].astype(np.float32)
|
||||
|
||||
fTrain = torch.from_numpy(x_train.values)
|
||||
tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
|
||||
|
||||
fTest= torch.from_numpy(x_test.values)
|
||||
tTest = torch.from_numpy(y_test.values)
|
||||
|
||||
batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
|
||||
num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
|
||||
learning_rate = 0.001
|
||||
input_dim = 6
|
||||
output_dim = 1
|
||||
|
||||
model = LogisticRegressionModel(input_dim, output_dim)
|
||||
|
||||
criterion = torch.nn.BCELoss(reduction='mean')
|
||||
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
|
||||
|
||||
for epoch in range(num_epochs):
|
||||
# print ("Epoch #",epoch)
|
||||
model.train()
|
||||
optimizer.zero_grad()
|
||||
# Forward pass
|
||||
y_pred = model(fTrain)
|
||||
# Compute Loss
|
||||
loss = criterion(y_pred, tTrain)
|
||||
# print(loss.item())
|
||||
# Backward pass
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
y_pred = model(fTest)
|
||||
# print("predicted Y value: ", y_pred.data)
|
||||
|
||||
|
||||
txt_file = open("Y_pred.txt", "w")
|
||||
n = txt_file.write(f"Y_pred: { y_pred.data}")
|
||||
txt_file.close()
|
||||
# torch.save(model.state_dict(), 'stroke.pth')
|
||||
|
Loading…
Reference in New Issue
Block a user