Update evaluate.py
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evaluate.py
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evaluate.py
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import pandas as pd
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valid = pd.read_csv("valid.csv")
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x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',
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'PhysicalActivities', 'SleepHours', 'RemovedTeeth',
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'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',
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'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',
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'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
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'DifficultyConcentrating', 'DifficultyWalking',
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'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',
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'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',
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'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',
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'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']
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y_column = 'HadHeartAttack'
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valid_x = valid[x_columns]
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valid_y = valid[y_column]
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from tensorflow import keras
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model = keras.models.load_model('model.keras')
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import numpy as np
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predictions = model.predict(valid_x)[:,0]
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true_answers = valid_y.to_numpy()
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validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)
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print(f"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}")
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np.savetxt("predictions.txt",predictions)
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np.savetxt("predictions_two_digits.txt",predictions, fmt='%1.2f')
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validate_heart_disease_true = valid.loc[valid[y_column]==1]
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validate_heart_disease_false = valid.loc[valid[y_column]==0]
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from datetime import timezone
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import datetime
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import json
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validate_heart_disease_true_x = validate_heart_disease_true[x_columns]
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validate_heart_disease_false_x = validate_heart_disease_false[x_columns]
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predictions_for_true = model.predict(validate_heart_disease_true_x)[:,0]
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predictions_for_false = model.predict(validate_heart_disease_false_x)[:,0]
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true_positives = np.sum(np.rint(predictions_for_true) == np.ones_like(predictions_for_true)).tolist()
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true_negatives = np.sum(np.rint(predictions_for_false) == np.zeros_like(predictions_for_false)).tolist()
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false_positives = len(predictions_for_false)-true_negatives
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false_negatives = len(predictions_for_true)-true_positives
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current_datetime = datetime.datetime.now(timezone.utc)
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metrics = {"true_positives": true_positives, "true_negatives": true_negatives, "false_positives": false_positives, "false_negatives" : false_negatives, "datetime_utc" : str(current_datetime)}
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history = []
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try:
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with open("metrics.json","r") as f:
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history = json.load(f)
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except FileNotFoundError:
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print('No historical metrics found')
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history.append(metrics)
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with open("metrics.json","w") as f:
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json.dump(history, f)
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import matplotlib.pyplot as plt
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true_positives_history = [x["true_positives"] for x in history]
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true_negatives_history = [x["true_negatives"] for x in history]
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false_positives_history = [x["false_positives"] for x in history]
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false_negatives_history = [x["false_negatives"] for x in history]
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plt.plot(true_positives_history, "-go")
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plt.plot(true_negatives_history, "-bo")
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plt.plot(false_positives_history, "-ro")
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plt.plot(false_negatives_history, "-mo")
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plt.legend(["True positives", "True negatives", "False positives", "False negatives"])
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plt.xlabel("Build number")
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plt.ylabel("Metric value")
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plt.title("Model evaluation history")
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import pandas as pd
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valid = pd.read_csv("valid.csv")
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x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',
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'PhysicalActivities', 'SleepHours', 'RemovedTeeth',
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'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',
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'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',
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'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
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'DifficultyConcentrating', 'DifficultyWalking',
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'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',
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'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',
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'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',
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'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']
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y_column = 'HadHeartAttack'
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valid.dropna(subset = x_columns, inplace=True)
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valid_x = valid[x_columns]
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valid_y = valid[y_column]
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from tensorflow import keras
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model = keras.models.load_model('model.keras')
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import numpy as np
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predictions = model.predict(valid_x)[:,0]
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true_answers = valid_y.to_numpy()
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validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)
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print(f"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}")
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np.savetxt("predictions.txt",predictions)
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np.savetxt("predictions_two_digits.txt",predictions, fmt='%1.2f')
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validate_heart_disease_true = valid.loc[valid[y_column]==1]
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validate_heart_disease_false = valid.loc[valid[y_column]==0]
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from datetime import timezone
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import datetime
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import json
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validate_heart_disease_true_x = validate_heart_disease_true[x_columns]
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validate_heart_disease_false_x = validate_heart_disease_false[x_columns]
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predictions_for_true = model.predict(validate_heart_disease_true_x)[:,0]
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predictions_for_false = model.predict(validate_heart_disease_false_x)[:,0]
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true_positives = np.sum(np.rint(predictions_for_true) == np.ones_like(predictions_for_true)).tolist()
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true_negatives = np.sum(np.rint(predictions_for_false) == np.zeros_like(predictions_for_false)).tolist()
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false_positives = len(predictions_for_false)-true_negatives
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false_negatives = len(predictions_for_true)-true_positives
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current_datetime = datetime.datetime.now(timezone.utc)
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metrics = {"true_positives": true_positives, "true_negatives": true_negatives, "false_positives": false_positives, "false_negatives" : false_negatives, "datetime_utc" : str(current_datetime)}
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history = []
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try:
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with open("metrics.json","r") as f:
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history = json.load(f)
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except FileNotFoundError:
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print('No historical metrics found')
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history.append(metrics)
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with open("metrics.json","w") as f:
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json.dump(history, f)
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import matplotlib.pyplot as plt
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true_positives_history = [x["true_positives"] for x in history]
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true_negatives_history = [x["true_negatives"] for x in history]
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false_positives_history = [x["false_positives"] for x in history]
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false_negatives_history = [x["false_negatives"] for x in history]
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plt.plot(true_positives_history, "-go")
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plt.plot(true_negatives_history, "-bo")
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plt.plot(false_positives_history, "-ro")
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plt.plot(false_negatives_history, "-mo")
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plt.legend(["True positives", "True negatives", "False positives", "False negatives"])
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plt.xlabel("Build number")
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plt.ylabel("Metric value")
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plt.title("Model evaluation history")
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plt.savefig("metrics.jpg")
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