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round

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Angelos Chatzimparmpas 4 years ago
parent 673edd1d77
commit c15b556c82
3 changed files with 275 additions and 1 deletions
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  1. BIN
      __pycache__/run.cpython-37.pyc
  2. 25
      frontend/src/components/Main.vue
  3. 251
      run.py

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__pycache__/run.cpython-37.pyc
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frontend/src/components/Main.vue
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@ -206,6 +206,7 @@ export default Vue.extend({
},
data () {
return {
correlResulTranformed: [],
PositiveValue: 75,
NegativeValue: 25,
unselectedRemainingPoints: [],
@ -474,7 +475,7 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the thresholds to the server side!')
this.returnCorrel()
this.returnCorrelTranformed()
})
.catch(error => {
console.log(error)
@ -503,7 +504,29 @@ export default Vue.extend({
.catch(error => {
console.log(error)
})
},
returnCorrelTranformed () {
const path = `http://127.0.0.1:5000/data/returnCorrelationsTransformed`
const axiosConfig = {
headers: {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
}
}
axios.get(path, axiosConfig)
.then(response => {
console.log('Server successfully send the correlation matrices!')
this.correlResulTranformed = response.data.correlResulTranformed
console.log(this.correlResulTranformed)
this.returnCorrel()
})
.catch(error => {
console.log(error)
})
}
},
created () {
// does the browser support the Navigation Timing API?

251
run.py
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@ -474,6 +474,245 @@ def executeModel():
return 'Everything Okay'
def Transformation(quadrant1, quadrant2, quadrant3, quadrant4):
XDataNumeric = XData.select_dtypes(include='number')
numberOfColumns = len(XDataNumeric.columns)
columns = list(XDataNumeric)
decimals = 0
global packCorrTransformed
packCorrTransformed = []
for i in columns:
dicTransf = {}
d={}
XDataNumericCopy = XDataNumeric.copy()
XDataNumericCopy[i] = XDataNumericCopy[i].round()
for number in range(1,5):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["round"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], quadrant1, quadrant2, quadrant3, quadrant4)
d={}
XDataNumericCopy = XDataNumeric.copy()
XDataNumericCopy[i] = np.log(XDataNumericCopy[i])
XDataNumericCopy[i] = XDataNumericCopy[i].round()
for number in range(1,5):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["roundLogE"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], quadrant1, quadrant2, quadrant3, quadrant4)
d={}
XDataNumericCopy = XDataNumeric.copy()
XDataNumericCopy[i] = np.log2(XDataNumericCopy[i])
XDataNumericCopy[i] = XDataNumericCopy[i].round()
for number in range(1,5):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["roundLog2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], quadrant1, quadrant2, quadrant3, quadrant4)
d={}
XDataNumericCopy = XDataNumeric.copy()
XDataNumericCopy[i] = np.log10(XDataNumericCopy[i])
XDataNumericCopy[i] = XDataNumericCopy[i].round()
for number in range(1,5):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["roundLog10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], quadrant1, quadrant2, quadrant3, quadrant4)
packCorrTransformed.append(dicTransf)
return 'Everything Okay'
def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, quadrant1, quadrant2, quadrant3, quadrant4):
# Main Features
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
DataRows1 = DataRows1.reset_index(drop=True)
DataRows2 = DataRows2.reset_index(drop=True)
DataRows3 = DataRows3.reset_index(drop=True)
DataRows4 = DataRows4.reset_index(drop=True)
targetRows1 = [yData[i] for i in quadrant1]
targetRows2 = [yData[i] for i in quadrant2]
targetRows3 = [yData[i] for i in quadrant3]
targetRows4 = [yData[i] for i in quadrant4]
targetRows1Arr = np.array(targetRows1)
targetRows2Arr = np.array(targetRows2)
targetRows3Arr = np.array(targetRows3)
targetRows4Arr = np.array(targetRows4)
uniqueTarget1 = unique(targetRows1)
uniqueTarget2 = unique(targetRows2)
uniqueTarget3 = unique(targetRows3)
uniqueTarget4 = unique(targetRows4)
if (len(targetRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targetRows1Arr = targetRows1Arr.reshape(len(targetRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targetRows1Arr)
hotEncoderDF1 = pd.DataFrame(onehotEncoder1)
concatDF1 = pd.concat([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-len(uniqueTarget1):]
X1 = add_constant(DataRows1.dropna())
VIF1 = pd.Series([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
if (len(targetRows1Arr) > 2):
MI1 = mutual_info_classif(DataRows1, targetRows1Arr)
MI1List = MI1.tolist()
else:
MI1List = []
else:
corrMatrixComb1 = pd.DataFrame()
VIF1 = pd.Series()
MI1List = []
if (len(targetRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targetRows2Arr = targetRows2Arr.reshape(len(targetRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targetRows2Arr)
hotEncoderDF2 = pd.DataFrame(onehotEncoder2)
concatDF2 = pd.concat([DataRows2, hotEncoderDF2], axis=1)
corrMatrixComb2 = concatDF2.corr()
corrMatrixComb2 = corrMatrixComb2.abs()
corrMatrixComb2 = corrMatrixComb2.iloc[:,-len(uniqueTarget2):]
X2 = add_constant(DataRows2.dropna())
VIF2 = pd.Series([variance_inflation_factor(X2.values, i)
for i in range(X2.shape[1])],
index=X2.columns)
if (len(targetRows2Arr) > 2):
MI2 = mutual_info_classif(DataRows2, targetRows2Arr)
MI2List = MI2.tolist()
else:
MI2List = []
else:
corrMatrixComb2 = pd.DataFrame()
VIF2 = pd.Series()
MI2List = []
if (len(targetRows3Arr) > 0):
onehotEncoder3 = OneHotEncoder(sparse=False)
targetRows3Arr = targetRows3Arr.reshape(len(targetRows3Arr), 1)
onehotEncoder3 = onehotEncoder3.fit_transform(targetRows3Arr)
hotEncoderDF3 = pd.DataFrame(onehotEncoder3)
concatDF3 = pd.concat([DataRows3, hotEncoderDF3], axis=1)
corrMatrixComb3 = concatDF3.corr()
corrMatrixComb3 = corrMatrixComb3.abs()
corrMatrixComb3 = corrMatrixComb3.iloc[:,-len(uniqueTarget3):]
X3 = add_constant(DataRows3.dropna())
VIF3 = pd.Series([variance_inflation_factor(X3.values, i)
for i in range(X3.shape[1])],
index=X3.columns)
if (len(targetRows3Arr) > 2):
MI3 = mutual_info_classif(DataRows3, targetRows3Arr)
MI3List = MI3.tolist()
else:
MI3List = []
else:
corrMatrixComb3 = pd.DataFrame()
VIF3 = pd.Series()
MI3List = []
if (len(targetRows4Arr) > 0):
onehotEncoder4 = OneHotEncoder(sparse=False)
targetRows4Arr = targetRows4Arr.reshape(len(targetRows4Arr), 1)
onehotEncoder4 = onehotEncoder4.fit_transform(targetRows4Arr)
hotEncoderDF4 = pd.DataFrame(onehotEncoder4)
concatDF4 = pd.concat([DataRows4, hotEncoderDF4], axis=1)
corrMatrixComb4 = concatDF4.corr()
corrMatrixComb4 = corrMatrixComb4.abs()
corrMatrixComb4 = corrMatrixComb4.iloc[:,-len(uniqueTarget4):]
X4 = add_constant(DataRows4.dropna())
VIF4 = pd.Series([variance_inflation_factor(X4.values, i)
for i in range(X4.shape[1])],
index=X4.columns)
if (len(targetRows4Arr) > 2):
MI4 = mutual_info_classif(DataRows4, targetRows4Arr)
MI4List = MI4.tolist()
else:
MI4List = []
else:
corrMatrixComb4 = pd.DataFrame()
VIF4 = pd.Series()
MI4List = []
targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
targetRows3ArrDF = pd.DataFrame(targetRows3Arr)
targetRows4ArrDF = pd.DataFrame(targetRows4Arr)
concatAllDF1 = pd.concat([DataRows1, targetRows1ArrDF], axis=1)
concatAllDF2 = pd.concat([DataRows2, targetRows2ArrDF], axis=1)
concatAllDF3 = pd.concat([DataRows3, targetRows3ArrDF], axis=1)
concatAllDF4 = pd.concat([DataRows4, targetRows4ArrDF], axis=1)
corrMatrixCombTotal1 = concatAllDF1.corr()
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
corrMatrixCombTotal2 = concatAllDF2.corr()
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
corrMatrixCombTotal3 = concatAllDF3.corr()
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
corrMatrixCombTotal4 = concatAllDF4.corr()
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
corrMatrixCombTotal1 = pd.concat([corrMatrixCombTotal1.tail(1)])
corrMatrixCombTotal2 = pd.concat([corrMatrixCombTotal2.tail(1)])
corrMatrixCombTotal3 = pd.concat([corrMatrixCombTotal3.tail(1)])
corrMatrixCombTotal4 = pd.concat([corrMatrixCombTotal4.tail(1)])
packCorrLoc = []
packCorrLoc.append(corrMatrix1.to_json())
packCorrLoc.append(corrMatrix2.to_json())
packCorrLoc.append(corrMatrix3.to_json())
packCorrLoc.append(corrMatrix4.to_json())
packCorrLoc.append(corrMatrixComb1.to_json())
packCorrLoc.append(corrMatrixComb2.to_json())
packCorrLoc.append(corrMatrixComb3.to_json())
packCorrLoc.append(corrMatrixComb4.to_json())
packCorrLoc.append(corrMatrixCombTotal1.to_json())
packCorrLoc.append(corrMatrixCombTotal2.to_json())
packCorrLoc.append(corrMatrixCombTotal3.to_json())
packCorrLoc.append(corrMatrixCombTotal4.to_json())
packCorrLoc.append(json.dumps(uniqueTarget1))
packCorrLoc.append(json.dumps(uniqueTarget2))
packCorrLoc.append(json.dumps(uniqueTarget3))
packCorrLoc.append(json.dumps(uniqueTarget4))
packCorrLoc.append(VIF1.to_json())
packCorrLoc.append(VIF2.to_json())
packCorrLoc.append(VIF3.to_json())
packCorrLoc.append(VIF4.to_json())
packCorrLoc.append(json.dumps(MI1List))
packCorrLoc.append(json.dumps(MI2List))
packCorrLoc.append(json.dumps(MI3List))
packCorrLoc.append(json.dumps(MI4List))
return packCorrLoc
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/thresholdDataSpace', methods=["GET", "POST"])
def Seperation():
@ -503,11 +742,14 @@ def Seperation():
else:
quadrant4.append(index)
Transformation(quadrant1, quadrant2, quadrant3, quadrant4)
DataRows1 = XData.iloc[quadrant1, :]
DataRows2 = XData.iloc[quadrant2, :]
DataRows3 = XData.iloc[quadrant3, :]
DataRows4 = XData.iloc[quadrant4, :]
# Main Features
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
@ -691,6 +933,15 @@ def Seperation():
return 'Everything Okay'
@app.route('/data/returnCorrelationsTransformed', methods=["GET", "POST"])
def SendCorrelTransformed():
global packCorrTransformed
response = {
'correlResulTranformed': packCorrTransformed
}
return jsonify(response)
@app.route('/data/returnCorrelations', methods=["GET", "POST"])
def SendCorrel():
global packCorr

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