test model with sample data
This commit is contained in:
Pritimay Sarkar
@@ -2,85 +2,91 @@ import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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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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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve, roc_auc_score
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import pickle
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import statsmodels.api as sm
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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_excel(curdir + path_delim + 'data/tests_24_10_2023_20_53_cleaned.xlsx', sheet_name="data")
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data = data.dropna()
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print(data)
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# curdir = os.getcwd()
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# path_delim = '/'
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# data = pd.read_excel(curdir + path_delim + 'data/tests_24_10_2023_20_53_cleaned.xlsx', sheet_name="data")
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# data = data.dropna()
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# # print(data)
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data.plot()
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# data.plot()
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label_encoder = LabelEncoder()
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categorical_cols = ['deviceId', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']
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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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# label_encoder = LabelEncoder()
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# categorical_cols = ['calculatedRatio', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']
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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[['deviceId', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']]
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y = data['classificationResult']
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# X = data[['calculatedRatio', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']]
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# y = data['classificationResult']
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corr = X.corr()
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print(corr)
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sm.graphics.plot_corr(corr, xnames=list(corr.columns))
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plt.show()
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# corr = X.corr()
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# print(corr)
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# sm.graphics.plot_corr(corr, xnames=list(corr.columns))
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# # plt.show()
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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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# 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 = LogisticRegression()
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model.fit(X_train, y_train)
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# model = LogisticRegression()
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# model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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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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#sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True)
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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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# # sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True)
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# Calculate the confusion matrix
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confusion = confusion_matrix(y_test, y_pred)
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# # Calculate the confusion matrix
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# confusion = confusion_matrix(y_test, y_pred)
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# Plot the confusion matrix using Seaborn
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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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# # Plot the confusion matrix using Seaborn
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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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# print(f"Accuracy: {accuracy}")
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# print("Classification Report:")
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# print(classification_report_result)
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# Accuracy: 0.6171938361719383
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# Classification Report:
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# precision recall f1-score support
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# # dataset: [6163 rows x 15 columns]
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# # Accuracy: 0.6593673965936739
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# # Classification Report:
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# # precision recall f1-score support
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# Inconclusive. Repeat with test with lower volume of blood 0.00 0.00 0.00 6
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# Inconclusive. Very low Absorbance - Repeat test with Higher Blood Volume 0.17 0.05 0.07 21
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# Negative Borderline. Repeat Test 0.00 0.00 0.00 167
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# Normal 0.69 0.89 0.78 749
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# Positive for Sickle Cell. HPLC for Confirmation 0.00 0.00 0.00 41
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# Sickle Cell Disease 0.28 0.24 0.26 38
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# Sickle Cell Trait 0.37 0.39 0.38 211
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# accuracy 0.62 1233
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# macro avg 0.22 0.22 0.21 1233
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# weighted avg 0.49 0.62 0.55 1233
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# # Inconclusive. Repeat with test with lower volume of blood 0.00 0.00 0.00 6
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# # Inconclusive. Very low Absorbance - Repeat test with Higher Blood Volume 0.88 0.71 0.79 21
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# # Negative Borderline. Repeat Test 0.11 0.01 0.01 167
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# # Normal 0.80 0.88 0.84 749
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# # Positive for Sickle Cell. HPLC for Confirmation 0.00 0.00 0.00 41
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# # Sickle Cell Disease 0.25 0.16 0.19 38
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# # Sickle Cell Trait 0.37 0.62 0.46 211
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# # accuracy 0.66 1233
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# # macro avg 0.34 0.34 0.33 1233
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# # weighted avg 0.58 0.66 0.61 1233
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# with open('logistic_regression_model.pkl', 'wb') as model_file:
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# pickle.dump(model, model_file)
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# with open('logistic_regression_model.pkl', 'rb') as model_file:
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# loaded_model = pickle.load(model_file)
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with open('logistic_regression_model.pkl', 'rb') as model_file:
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loaded_model = pickle.load(model_file)
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# new_data = pd.DataFrame({'Age': [30], 'Gender': ['MALE'], 'Caste': ['SC'], 'Category': [''], 'Marital Status': ['Single']})
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# predicted_result = loaded_model.predict(new_data)
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# print(predicted_result)
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# new_data = pd.DataFrame({'calculatedRatio': [0.126976079], 'led1Buffer': [24313.67], 'led1Sample': [20531], 'led2Buffer': [26565], 'led2Sample': [9975.33]}) #Normal
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# new_data = pd.DataFrame({'calculatedRatio': [0.17500836], 'led1Buffer': [24843.33], 'led1Sample': [19678], 'led2Buffer': [26715.33], 'led2Sample': [13842.67]}) #SCT
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# new_data = pd.DataFrame({'calculatedRatio': [0.251395102], 'led1Buffer': [24244], 'led1Sample': [17475], 'led2Buffer': [27059.67], 'led2Sample': [9258.33]}) #SCD
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# new_data = pd.DataFrame({'calculatedRatio': [0.251061035], 'led1Buffer': [24172], 'led1Sample': [19345], 'led2Buffer': [26918], 'led2Sample': [12964.33]})
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# new_data = pd.DataFrame({'calculatedRatio': [0.242851779], 'led1Buffer': [25087.33], 'led1Sample': [20170.67], 'led2Buffer': [26578.33], 'led2Sample': [12724.67]})
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# new_data = pd.DataFrame({'calculatedRatio': [0.189778691], 'led1Buffer': [23209.33], 'led1Sample': [17672], 'led2Buffer': [19850.33], 'led2Sample': [10360.33]}) #SCT
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new_data = pd.DataFrame({'calculatedRatio': [0.149174719], 'led1Buffer': [24122], 'led1Sample': [18368.33], 'led2Buffer': [21733.33], 'led2Sample': [9114.67]}) #Normal
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predicted_result = loaded_model.predict(new_data)
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print(predicted_result)
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