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introduced heatmap for data

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master
Angelos Chatzimparmpas 5 years ago
parent 655408f595
commit fdbaed08dc
9 changed files with 161 additions and 98 deletions
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{"duration": 275.9160199165344, "input_args": {"clf": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "0"}}

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{"duration": 355.3933901786804, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n max_depth=None, max_features='auto', max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, n_estimators=119,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "params": "{'n_estimators': [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "576"}}

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# first line: 371
# first line: 373
@memory.cache
def GridSearchForModels(clf, params, eachAlgor, factors, AlgorithmsIDsEnd):

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}
},
"pacote": {
@ -19817,9 +19831,9 @@
}
},
"popper.js": {
"version": "1.16.0",
"resolved": "https://registry.npmjs.org/popper.js/-/popper.js-1.16.0.tgz",
"integrity": "sha512-+G+EkOPoE5S/zChTpmBSSDYmhXJ5PsW8eMhH8cP/CQHMFPBG/kC9Y5IIw6qNYgdJ+/COf0ddY2li28iHaZRSjw=="
"version": "1.16.1",
"resolved": "https://registry.npmjs.org/popper.js/-/popper.js-1.16.1.tgz",
"integrity": "sha512-Wb4p1J4zyFTbM+u6WuO4XstYx4Ky9Cewe4DWrel7B0w6VVICvPwdOpotjzcf6eD8TsckVnIMNONQyPIUFOUbCQ=="
},
"portal-vue": {
"version": "2.1.7",
@ -26138,13 +26152,21 @@
}
},
"vue-plotly": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/vue-plotly/-/vue-plotly-1.0.1.tgz",
"integrity": "sha512-JNBQeTgiHIomnGhwL+2FkqY/6qiIU3mNRiXBNVOmx6xyrx657H6QxeNL9YxSrKpN4wvR/ulnwztKVKO5aT6Fqg==",
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/vue-plotly/-/vue-plotly-1.1.0.tgz",
"integrity": "sha512-GW52p+ZD0AKHgtE7vdkljJ79fiRugkhFkcE+Es5Ok9YWulHq8KxywQdO0rK1ejGIfVv1yAQKiKxhzYcYE/lXyw==",
"requires": {
"plotly.js": "^1.49.4",
"vue": "^2.6.10",
"core-js": "^3.6.4",
"plotly.js": "^1.52.1",
"vue": "^2.6.11",
"vue-resize-directive": "^1.2.0"
},
"dependencies": {
"core-js": {
"version": "3.6.4",
"resolved": "https://registry.npmjs.org/core-js/-/core-js-3.6.4.tgz",
"integrity": "sha512-4paDGScNgZP2IXXilaffL9X7968RuvwlkK3xWtZRVqgd8SYNiVKRJvkFd1aqqEuPfN7E68ZHEp9hDj6lHj4Hyw=="
}
}
},
"vue-plugin-load-script": {

14
frontend/package.json
Unescape View File

@ -19,16 +19,16 @@
"@fortawesome/vue-fontawesome": "^0.1.9",
"@statnett/vue-plotly": "^0.3.2",
"@types/d3-drag": "^1.2.3",
"@types/node": "^13.1.8",
"@types/node": "^13.5.0",
"ajv": "^6.11.0",
"audit": "0.0.6",
"axios": "^0.19.1",
"axios": "^0.19.2",
"axios-progress-bar": "^1.2.0",
"babel-preset-vue": "^2.0.2",
"blob": "0.0.5",
"bootstrap": "^4.4.1",
"bootstrap-toggle": "^2.2.2",
"bootstrap-vue": "^2.2.2",
"bootstrap-vue": "^2.3.0",
"circlepack-chart": "^1.3.0",
"clean-webpack-plugin": "^3.0.0",
"colorbrewer": "^1.3.0",
@ -46,15 +46,15 @@
"fs": "0.0.2",
"fs-es6": "0.0.2",
"ify-loader": "^1.1.0",
"interactjs": "^1.7.3",
"interactjs": "^1.8.2",
"jquery": "^3.4.1",
"mdbvue": "^6.2.0",
"mdbvue": "^6.3.0",
"mini-css-extract-plugin": "^0.9.0",
"npm-check-updates": "^4.0.1",
"papaparse": "^5.1.1",
"parcoord-es": "^2.2.10",
"plotly.js": "^1.52.1",
"popper.js": "^1.16.0",
"popper.js": "^1.16.1",
"react": "^16.12.0",
"react-dom": "^16.12.0",
"stardust-allofw": "^0.1.1",
@ -68,7 +68,7 @@
"vue": "^2.6.11",
"vue-bootstrap-slider": "^2.1.8",
"vue-papa-parse": "^1.3.0",
"vue-plotly": "^1.0.1",
"vue-plotly": "^1.1.0",
"vue-router": "^3.1.5",
"vue-slider-component": "^3.1.0",
"vue2-simplert-plugin": "^0.5.3",

45
run.py
Unescape View File

@ -17,6 +17,7 @@ import ast
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from yellowbrick.regressor import CooksDistance
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline
@ -62,6 +63,9 @@ def Reset():
global factors
factors = [1,1,1,1,1]
global restoreClicked
restoreClicked = False
global XData
XData = []
@ -134,6 +138,9 @@ def RetrieveFileName():
global DataRawLength
global DataResultsRaw
global restoreClicked
restoreClicked = False
global RANDOM_SEED
RANDOM_SEED = 42
@ -1162,6 +1169,9 @@ def EnsembleModel(Models, keyRetrieved):
global all_classifiersSelection
all_classifiersSelection = []
global XData
global yData
lr = LogisticRegression()
if (keyRetrieved == 0):
@ -1282,9 +1292,28 @@ def EnsembleModel(Models, keyRetrieved):
# random_state=RANDOM_SEED,
# n_jobs = -1)
# parallelize all that
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring='accuracy', n_jobs=-1)
scores.append(temp.mean())
scores.append(temp.std())
# influence calculation for all the instances
#DataHeatmap = []
#for indexValue, row in XData.iterrows():
# XDataRemove = XData.copy()
# XDataRemove.drop(indexValue, inplace=True)
# yDataRemove = yData.copy()
# del yDataRemove[indexValue]
# tempRemove = model_selection.cross_val_score(sclf, XDataRemove, yDataRemove, cv=crossValidation, scoring='accuracy', n_jobs=-1)
# DataHeatmap.append(abs((tempRemove.mean()+tempRemove.std())-(temp.mean()+temp.std())))
#print(DataHeatmap)
#averageValueData = sum(DataHeatmap) / len(DataHeatmap)
#print(averageValueData)
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring='precision_weighted', n_jobs=-1)
scores.append(temp.mean())
scores.append(temp.std())
@ -1300,6 +1329,7 @@ def EnsembleModel(Models, keyRetrieved):
temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring='recall_weighted', n_jobs=-1)
scores.append(temp.mean())
scores.append(temp.std())
return 'Okay'
# Sending the final results to be visualized as a line plot
@ -1352,6 +1382,12 @@ def RetrieveAction():
else:
median = XData.iloc[dataSpacePointsIDs, :].median()
XData.loc[len(XData)]= median
yDataSelected = [yData[i] for i in dataSpacePointsIDs]
storeMode = mode(yDataSelected)
yData.append(storeMode)
XData = XData.drop(dataSpacePointsIDs)
yData = [i for j, i in enumerate(yData) if j not in dataSpacePointsIDs]
XData.reset_index(drop=True, inplace=True)
elif (filterActionFinal == 'compose'):
if (filterDataFinal == 'mean' or filterDataFinal == ''):
mean = XData.iloc[dataSpacePointsIDs, :].mean()
@ -1366,8 +1402,8 @@ def RetrieveAction():
XData = XData.drop(dataSpacePointsIDs)
yData = [i for j, i in enumerate(yData) if j not in dataSpacePointsIDs]
print(XData)
print(yData)
return 'Done'
# Retrieve data from client
@ -1386,7 +1422,10 @@ def RetrieveProvenance():
# fix save and restore
if (filterProvenanceFinal == 'restore'):
if (filterProvenanceFinal == 'save'):
XDataStored = XData
yDataStored = yData
else:
XData = XDataStored.copy()
yData = yDataStored.copy()
return 'Done'
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