99 lines
4.2 KiB
Python
99 lines
4.2 KiB
Python
import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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import pickle
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import matplotlib.pyplot as plt
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import seaborn as sns
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import os
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curdir = os.getcwd()
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path_delim = '/'
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data = pd.read_csv(curdir + path_delim + 'data/bquxjob_6ffc1cff_18c02a130c1.csv')
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data = data.dropna()
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print(data)
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data.plot()
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label_encoder = LabelEncoder()
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categorical_cols = [] #['deviceId', 'deviceSerialNumber', 'classificationResult']
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for col in categorical_cols:
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data[col] = label_encoder.fit_transform(data[col])
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X = data[['calculatedRatio', 'deviceRatio', 'led1Buffer', 'led2Buffer', 'led1Sample', 'led2Sample']] #, 'deviceId', 'deviceSerialNumber']]
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y = data['classificationResult']
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print(X)
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print(y)
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# Fit the LabelEncoder on the entire column
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# label_encoder.fit(data['classificationResult'])
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with open('label_encoder.pkl', 'wb') as label_encoder_file:
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pickle.dump(label_encoder, label_encoder_file)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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model = GaussianNB()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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classification_report_result = classification_report(y_test, y_pred)
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confusion = confusion_matrix(y_test, y_pred)
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plt.figure(figsize=(8, 6))
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sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues', linewidths=0.5)
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plt.xlabel('Predicted')
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plt.ylabel('Actual')
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plt.title('Confusion Matrix')
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# plt.show()
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print(f"Accuracy: {accuracy}")
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print("Classification Report:")
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print(classification_report_result)
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with open('gaussian_naive_bayes_model.pkl', 'wb') as model_file:
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pickle.dump(model, model_file)
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with open('label_encoder.pkl', 'rb') as label_encoder_file:
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loaded_label_encoder = pickle.load(label_encoder_file)
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# print(loaded_label_encoder.classes_)
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# print(loaded_label_encoder.label)
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with open('gaussian_naive_bayes_model.pkl', 'rb') as model_file:
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loaded_model = pickle.load(model_file)
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# loaded_label_encoder = label_encoder
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# loaded_model = model
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# new_data = pd.DataFrame({'calculatedRatio': label_encoder.fit_transform([0.126976079]), 'led1Buffer': label_encoder.fit_transform([24313.67]), 'led1Sample': label_encoder.fit_transform([20531]), 'led2Buffer': label_encoder.fit_transform([26565]), 'led2Sample': label_encoder.fit_transform([9975.33])})
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# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.17500836]), 'led1Buffer': loaded_label_encoder.fit_transform([24843.33]), 'led1Sample': loaded_label_encoder.fit_transform([19678]), 'led2Buffer': loaded_label_encoder.fit_transform([26715.33]), 'led2Sample': loaded_label_encoder.fit_transform([13842.67])})
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# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.175881142]), 'led1Buffer': loaded_label_encoder.fit_transform([24256]), 'led1Sample': loaded_label_encoder.fit_transform([16303]), 'led2Buffer': loaded_label_encoder.fit_transform([27016.33]), 'led2Sample': loaded_label_encoder.fit_transform([4612.67])})
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# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.126976079]), 'led1Buffer': loaded_label_encoder.fit_transform([24313.67]), 'led1Sample': loaded_label_encoder.fit_transform([20531]), 'led2Buffer': loaded_label_encoder.fit_transform([26565]), 'led2Sample': loaded_label_encoder.fit_transform([9975.33])})
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new_data = pd.DataFrame({'calculatedRatio': 0.231057205, 'deviceRatio': 0.231057205,'led1Buffer': 23776.33, 'led2Buffer': 26401.67,
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'led1Sample': 16286, 'led2Sample': 6952.67}, index=[0])
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# new_data = pd.DataFrame({
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# 'calculatedRatio': [0.175881142],
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# 'led1Buffer': [24256],
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# 'led1Sample': [16303],
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# 'led2Buffer': [27016.33],
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# 'led2Sample': [4612.67]
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# })
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print(new_data)
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# new_data = new_data.apply(lambda col: loaded_label_encoder.transform(col))
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predicted_result = loaded_model.predict(new_data)
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print(predicted_result.item())
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X_test_encoded = X_test.copy()
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for col in categorical_cols:
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X_test_encoded[col] = loaded_label_encoder.transform(X_test[col])
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print(X_test_encoded)
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y_pred = model.predict(X_test_encoded)
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print(pd.Series(y_pred).describe()) |