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Commit f1976ef0 authored by Aayush Saxena's avatar Aayush Saxena
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PartA.py 0 → 100644
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import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,f1_score
import warnings
warnings.filterwarnings('ignore')
# Importing necessary libraries
# Importing our training and testing csv files
pd.set_option('display.max_columns', None)
testingData= pd.read_csv('Datasets/TestingDataBinary.csv', header=None)
trainingData= pd.read_csv('Datasets/TrainingDataBinary.csv', header=None)
# These are the column names as decribed in the problem statement
columns = ['R1-PA1:VH', 'R1-PM1:V', 'R1-PA2:VH', 'R1-PM2:V', 'R1-PA3:VH', 'R1-PM3:V', 'R1-PA4:IH', 'R1-PM4:I', 'R1-PA5:IH', 'R1-PM5:I', 'R1-PA6:IH', 'R1-PM6:I', 'R1-PA7:VH', 'R1-PM7:V', 'R1-PA8:VH', 'R1-PM8:V', 'R1-PA9:VH', 'R1-PM9:V', 'R1-PA10:IH', 'R1-PM10:I', 'R1-PA11:IH', 'R1-PM11:I', 'R1-PA12:IH', 'R1-PM12:I', 'R1:F', 'R1:DF', 'R1-PA:Z', 'R1-PA:ZH', 'R1:S', 'R2-PA1:VH', 'R2-PM1:V', 'R2-PA2:VH', 'R2-PM2:V', 'R2-PA3:VH', 'R2-PM3:V', 'R2-PA4:IH', 'R2-PM4:I', 'R2-PA5:IH', 'R2-PM5:I', 'R2-PA6:IH', 'R2-PM6:I', 'R2-PA7:VH', 'R2-PM7:V', 'R2-PA8:VH', 'R2-PM8:V', 'R2-PA9:VH', 'R2-PM9:V', 'R2-PA10:IH', 'R2-PM10:I', 'R2-PA11:IH', 'R2-PM11:I', 'R2-PA12:IH', 'R2-PM12:I', 'R2:F', 'R2:DF', 'R2-PA:Z', 'R2-PA:ZH', 'R2:S', 'R3-PA1:VH', 'R3-PM1:V', 'R3-PA2:VH', 'R3-PM2:V', 'R3-PA3:VH', 'R3-PM3:V', 'R3-PA4:IH', 'R3-PM4:I', 'R3-PA5:IH', 'R3-PM5:I', 'R3-PA6:IH', 'R3-PM6:I', 'R3-PA7:VH', 'R3-PM7:V', 'R3-PA8:VH', 'R3-PM8:V', 'R3-PA9:VH', 'R3-PM9:V', 'R3-PA10:IH', 'R3-PM10:I', 'R3-PA11:IH', 'R3-PM11:I', 'R3-PA12:IH', 'R3-PM12:I', 'R3:F', 'R3:DF', 'R3-PA:Z', 'R3-PA:ZH', 'R3:S', 'R4-PA1:VH', 'R4-PM1:V', 'R4-PA2:VH', 'R4-PM2:V', 'R4-PA3:VH', 'R4-PM3:V', 'R4-PA4:IH', 'R4-PM4:I', 'R4-PA5:IH', 'R4-PM5:I', 'R4-PA6:IH', 'R4-PM6:I', 'R4-PA7:VH', 'R4-PM7:V', 'R4-PA8:VH', 'R4-PM8:V', 'R4-PA9:VH', 'R4-PM9:V', 'R4-PA10:IH', 'R4-PM10:I', 'R4-PA11:IH', 'R4-PM11:I', 'R4-PA12:IH', 'R4-PM12:I', 'R4:F', 'R4:DF', 'R4-PA:Z', 'R4-PA:ZH', 'R4:S', 'control_panel_log1', 'control_panel_log2', 'control_panel_log3', 'control_panel_log4', 'relay1_log', 'relay2_log', 'relay3_log', 'relay4_log', 'snort_log1', 'snort_log2', 'snort_log3', 'snort_log4']
trainingData.columns = columns + ["marker"]
testingData.columns = columns
# Getting the data ready for fitting, using the 128 features for training and the last column 'marker' for the testing and training
X = trainingData.drop('marker',axis=1)
y = trainingData['marker']
# Declaring the variables for testing and training using an 80-20 split
X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=.2, random_state=21)
# Our declaration for the random forest model
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)
# Fitting the model for training
y_pred = rfc.predict(X_test)
# Prediction using the X_test variable
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Checking and printing the accuracy of our predictions using the testing y_test variable
Marker = rfc.predict(testingData)
# Using the testing data csv to find the predicted values using our trained model
resultData = pd.DataFrame(Marker, columns=['PredictedMarker'])
# Save the predictions to a csv file
testingData['PredictedMarker'] = resultData["PredictedMarker"].tolist()
testingData.to_csv("TestingResultsBinary.csv", index=False)
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import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,f1_score
import warnings
warnings.filterwarnings('ignore')
# Importing necessary libraries
# Importing our training and testing csv files
pd.set_option('display.max_columns', None)
testingData = pd.read_csv('Datasets/TestingDataMulti.csv', header=None)
trainingData = pd.read_csv('Datasets/TrainingDataMulti.csv', header=None)
# These are the column names as decribed in the problem statement
columns = ['R1-PA1:VH', 'R1-PM1:V', 'R1-PA2:VH', 'R1-PM2:V', 'R1-PA3:VH', 'R1-PM3:V', 'R1-PA4:IH', 'R1-PM4:I', 'R1-PA5:IH', 'R1-PM5:I', 'R1-PA6:IH', 'R1-PM6:I', 'R1-PA7:VH', 'R1-PM7:V', 'R1-PA8:VH', 'R1-PM8:V', 'R1-PA9:VH', 'R1-PM9:V', 'R1-PA10:IH', 'R1-PM10:I', 'R1-PA11:IH', 'R1-PM11:I', 'R1-PA12:IH', 'R1-PM12:I', 'R1:F', 'R1:DF', 'R1-PA:Z', 'R1-PA:ZH', 'R1:S', 'R2-PA1:VH', 'R2-PM1:V', 'R2-PA2:VH', 'R2-PM2:V', 'R2-PA3:VH', 'R2-PM3:V', 'R2-PA4:IH', 'R2-PM4:I', 'R2-PA5:IH', 'R2-PM5:I', 'R2-PA6:IH', 'R2-PM6:I', 'R2-PA7:VH', 'R2-PM7:V', 'R2-PA8:VH', 'R2-PM8:V', 'R2-PA9:VH', 'R2-PM9:V', 'R2-PA10:IH', 'R2-PM10:I', 'R2-PA11:IH', 'R2-PM11:I', 'R2-PA12:IH', 'R2-PM12:I', 'R2:F', 'R2:DF', 'R2-PA:Z', 'R2-PA:ZH', 'R2:S', 'R3-PA1:VH', 'R3-PM1:V', 'R3-PA2:VH', 'R3-PM2:V', 'R3-PA3:VH', 'R3-PM3:V', 'R3-PA4:IH', 'R3-PM4:I', 'R3-PA5:IH', 'R3-PM5:I', 'R3-PA6:IH', 'R3-PM6:I', 'R3-PA7:VH', 'R3-PM7:V', 'R3-PA8:VH', 'R3-PM8:V', 'R3-PA9:VH', 'R3-PM9:V', 'R3-PA10:IH', 'R3-PM10:I', 'R3-PA11:IH', 'R3-PM11:I', 'R3-PA12:IH', 'R3-PM12:I', 'R3:F', 'R3:DF', 'R3-PA:Z', 'R3-PA:ZH', 'R3:S', 'R4-PA1:VH', 'R4-PM1:V', 'R4-PA2:VH', 'R4-PM2:V', 'R4-PA3:VH', 'R4-PM3:V', 'R4-PA4:IH', 'R4-PM4:I', 'R4-PA5:IH', 'R4-PM5:I', 'R4-PA6:IH', 'R4-PM6:I', 'R4-PA7:VH', 'R4-PM7:V', 'R4-PA8:VH', 'R4-PM8:V', 'R4-PA9:VH', 'R4-PM9:V', 'R4-PA10:IH', 'R4-PM10:I', 'R4-PA11:IH', 'R4-PM11:I', 'R4-PA12:IH', 'R4-PM12:I', 'R4:F', 'R4:DF', 'R4-PA:Z', 'R4-PA:ZH', 'R4:S', 'control_panel_log1', 'control_panel_log2', 'control_panel_log3', 'control_panel_log4', 'relay1_log', 'relay2_log', 'relay3_log', 'relay4_log', 'snort_log1', 'snort_log2', 'snort_log3', 'snort_log4']
trainingData.columns = columns + ["marker"]
testingData.columns = columns
# Getting the data ready for fitting, using the 128 features for training and the last column 'marker' for the testing and training
X = trainingData.drop('marker',axis=1)
y = trainingData['marker']
# Declaring the variables for testing and training using an 80-20 split
X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=.2, random_state=21)
# Our declaration for the random forest model
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)
# Fitting the model for training
y_pred = rfc.predict(X_test)
# Prediction using the X_test variable
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Checking and printing the accuracy of our predictions using the testing y_test variable
marker = rfc.predict(testingData)
# Using the testing data csv to find the predicted values using our trained model
resultData = pd.DataFrame(marker, columns=['PredictedMarker'])
# Save the predictions to a csv file
testingData['PredictedMarker'] = resultData["PredictedMarker"].tolist()
testingData.to_csv("TestingResultsMulti.csv", index=False)
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