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| # %% | |
| import shap | |
| import pandas as pd | |
| import numpy as np | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import os | |
| # %% | |
| from joblib import load | |
| # Assuming 'model' is your trained model | |
| model = load('model_trained.joblib') | |
| # %% | |
| import pandas as pd | |
| # First set of data | |
| data1 = {'ratio':[0.053910], | |
| 'sunset_minutes': [1080.968557], | |
| 'Kitchen_night_temperature':[20.784371], | |
| 'uvindex':[4.365996],'visibility':[15.912960], | |
| 'illuminance':[10646.052788] , | |
| 'Kitchen_night_illuminance':[76.282996], | |
| 'Lounge_night_illuminance':[83.069952] , | |
| 'Bathroom_night_temperature':[20.639559], | |
| 'Bathroom_evening_temperature':[21.449295], | |
| 'Bathroom_night_activity':[19.367503], | |
| 'number_transitions':[222.017461], | |
| 'temperature_ratio':[2.004192], | |
| 'awake_ratio':[0.207502], | |
| 'rr_min':[9.178120], | |
| 'To_bed' :[161.162813], | |
| 'Lounge_afternoon_activity':[46.898529], | |
| 'rr_average':[14.402949], | |
| 'rr_min_std':[0.696502], | |
| 'Kitchen_afternoon_illuminance':[371.943435]} | |
| data2 = {'ratio' :[0.030281], | |
| 'sunset_minutes' : [84.442677] , | |
| 'Kitchen_night_temperature' :[2.082688], | |
| 'uvindex': [2.701415], | |
| 'visibility': [5.435793], | |
| 'illuminance': [7337.315382], | |
| 'Kitchen_night_illuminance': [54.300962], | |
| 'Lounge_night_illuminance' :[116.355455], | |
| 'Bathroom_night_temperature':[2.007301], | |
| 'Bathroom_evening_temperature':[2.087516], | |
| 'Bathroom_night_activity':[35.206093] , | |
| 'number_transitions':[94.177603], | |
| 'temperature_ratio':[4.045142], | |
| 'awake_ratio':[0.061002], | |
| 'rr_min' :[0.428977], | |
| 'To_bed':[10.510216], | |
| 'Lounge_afternoon_activity':[174.547164], | |
| 'rr_average':[0.599838], | |
| 'rr_min_std':[0.299745], | |
| 'Kitchen_afternoon_illuminance':[255.993848]} | |
| mean_df = pd.DataFrame(data1) | |
| std_df = pd.DataFrame(data2) | |
| plot_feature_names = { | |
| "uvindex": "UV index", | |
| "sunset_minutes": "Sunset time", | |
| "temperature_ratio": "Temperature ratio", | |
| "ratio": "Illuminance ratio", | |
| "illuminance": "Outdoor illuminance", | |
| "cloudcover": "Cloudcover", | |
| "visibility": "Visibility", | |
| "number_transitions": "Number of transitions", | |
| 'awake_ratio' : 'Awake ratio', | |
| 'rr_min' : 'Minimum respiratory rate', | |
| 'rr_average' :'Average respiratory rate', | |
| 'rr_min_std' :'Variability of respiratory rate',"To_bed":"Bedtime","ratio":"Illuminance ratio"} | |
| # %% | |
| def normalize(example_data_df, mean_df, std_df, top_features): | |
| scale_global = example_data_df | |
| scale_global = (scale_global- mean_df) / std_df | |
| scale_global = scale_global.dropna(subset=scale_global.columns, how='all') | |
| return scale_global | |
| # %% | |
| # %% | |
| top_features= ['ratio', | |
| 'sunset_minutes', | |
| 'Kitchen_night_temperature', | |
| 'uvindex', | |
| 'visibility', | |
| 'illuminance', | |
| 'Kitchen_night_illuminance', | |
| 'Lounge_night_illuminance', | |
| 'Bathroom_night_temperature', | |
| 'Bathroom_evening_temperature', | |
| 'Bathroom_night_activity', | |
| 'number_transitions', | |
| 'temperature_ratio', | |
| 'awake_ratio', | |
| 'rr_min', | |
| 'To_bed', | |
| 'Lounge_afternoon_activity', | |
| 'rr_average', | |
| 'rr_min_std', | |
| 'Kitchen_afternoon_illuminance'] | |
| # %% | |
| def update_sliders(example_df): | |
| example_data= example_df | |
| Kitchen_afternoon_illuminance = example_data['Kitchen_afternoon_illuminance'].astype(float).iloc[-1] | |
| Kitchen_night_illuminance = example_data['Kitchen_night_illuminance'].astype(float).iloc[-1] | |
| Lounge_night_illuminance = example_data['Lounge_night_illuminance'].astype(float).iloc[-1] | |
| ratio = example_data['ratio'].astype(float).iloc[-1] | |
| Kitchen_night_temperature= example_data['Kitchen_night_temperature'].astype(float).iloc[-1] | |
| Bathroom_night_temperature= example_data['Bathroom_night_temperature'].astype(float).iloc[-1] | |
| Bathroom_evening_temperature= example_data['Bathroom_evening_temperature'].astype(float).iloc[-1] | |
| temperature_ratio = example_data['temperature_ratio'].astype(float).iloc[-1] | |
| return Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| # %% | |
| sns.set_palette("Set1") | |
| blue = sns.color_palette()[0] | |
| red = sns.color_palette()[1] | |
| # %% | |
| def predict(example_df,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio): | |
| example_data = example_df | |
| example_df_norm = normalize(example_data, mean_df, std_df, top_features) | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| df = example_df_norm[top_features] | |
| pos_pred = model.predict(df) | |
| proba = model.predict_proba(df) | |
| overall_prediction = "positive" if pos_pred[0] == 1 else "negative" | |
| class_index = 1 if overall_prediction == "positive" else 0 | |
| shap_values = explainer.shap_values(df) | |
| class_shap_values = shap_values[class_index][0] | |
| all_class_features = zip(class_shap_values, top_features, feature_values) | |
| sorted_class_features = sorted(all_class_features, key=lambda x: (x[0]), reverse=True) | |
| top_three_class_features = sorted_class_features[:3] | |
| statements = [] | |
| if proba[0][1] > 0.7940: | |
| statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:") | |
| if 0.3437 < proba[0][1] <= 0.7940: | |
| statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:") | |
| if proba[0][1] <= 0.3437: | |
| statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:") | |
| for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features): | |
| if feature_value > 0: | |
| feature_direction = "positive" | |
| else: | |
| feature_direction = "negative" | |
| # Additional information based on the feature and its direction | |
| if feature == "ratio": | |
| if feature_direction == "positive": | |
| statement= "The environment had a higher illuminance ratio (better light quality)." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment had a lower illuminance ratio (worse light quality, perhaps accompanied by glare)." | |
| statements.append(statement) | |
| elif feature == "awake_ratio": | |
| if feature_direction == "positive": | |
| statement= "The patient was alert during their sleep." | |
| statements.append(statement) | |
| else: | |
| statement= "The patient had lower levels of alertness during their sleep." | |
| statements.append(statement) | |
| elif feature == "rr_average": | |
| if feature_direction == "positive": | |
| statement= "The average respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The average respiratory rate during sleep was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "Lounge_night_illuminance": | |
| if feature_direction == "positive": | |
| statement= "There was higher illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| else: | |
| statement= "There was lower illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| elif feature == "Bathroom_evening_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the evening was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the evening was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_afternoon_illuminance": | |
| if feature_direction == "positive": | |
| statement = "The indoor light exposure in the kitchen in the afternoon was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The indoor light exposure in the kitchen in the afternoon was lower than usual." | |
| statements.append(statement) | |
| elif feature == "number_transitions": | |
| if feature_direction == "positive": | |
| statement= "The patient had increased movements from room to room in the house." | |
| statements.append(statement) | |
| else: | |
| statement="The patient tended to stay within specific rooms and did not move a lot from room to room." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| elif feature == "Lounge_afternoon_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement="The indoor light exposure in the kitchen at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The indoor light exposure in the kitchen at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement="The temperature in the bathroom at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min": | |
| if feature_direction == "positive": | |
| statement="The minimum respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The minimum respiratory rate was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "visibility": | |
| if feature_direction == "positive": | |
| statement= "The sky visibility was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The sky visibility was lower than usual." | |
| statements.append(statement) | |
| elif feature == "sunset_minutes": | |
| if feature_direction == "positive": | |
| statement= "The time of sunset was later than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The time of sunset was earlier than usual." | |
| statements.append(statement) | |
| elif feature == "uvindex": | |
| if feature_direction == "positive": | |
| statement= "The UV index was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The UV index was lower than usual." | |
| statements.append(statement) | |
| elif feature == "illuminance": | |
| if feature_direction == "positive": | |
| statement="Outdoor illuminance was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statements="Outdoor illuminance was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement = "There was higher illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| else: | |
| statement="There was lower illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| elif feature == "temperature_ratio": | |
| if feature_direction == "positive": | |
| statement = "The environment a smaller difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment a bigger difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min_std": | |
| if feature_direction == "positive": | |
| statement = "The variability in the minimum respiratory rate through the week was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The variability in the minimum respiratory rate through the week was lower than usual" | |
| statements.append(statement) | |
| elif feature == "To_bed": | |
| if feature_direction == "positive": | |
| statement = "The bedtimes of the participant during the week were more inconsistent than usual" | |
| statements.append(statement) | |
| else: | |
| statement= "The bedtimes of the participant during the week were more consistent than usual" | |
| statements.append(statement) | |
| html_output = "<br>".join(statements) | |
| formatted_statements = gr.HTML(html_output) | |
| return {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])}, formatted_statements | |
| # %% | |
| import shap | |
| import matplotlib.pyplot as plt | |
| def interpret(example_df,Lounge_night_illuminance, Kitchen_night_illuminance, Kitchen_afternoon_illuminance, ratio, temperature_ratio, Bathroom_night_temperature, Bathroom_evening_temperature, Kitchen_night_temperature): | |
| # Read the example data | |
| example_data = example_df | |
| # Normalize the example data | |
| example_df_norm = normalize(example_data, mean_df, std_df, top_features) | |
| # Select top features | |
| df = example_df_norm[top_features] | |
| # Make predictions | |
| predictions = model.predict(df) | |
| # Calculate SHAP values for all data points | |
| shap_values = explainer.shap_values(df) | |
| base = explainer.expected_value | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| # Plotting for positive class predictions (class label 1) | |
| if predictions[0] == 1: | |
| pos_shap_values = shap_values[1][0] | |
| scores_desc = list(zip(pos_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] < 0 else blue for s in scores_desc] | |
| plot = plt.figure(tight_layout=True, figsize=(18,18)) | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.08 if score[0] < 0 else bar.get_width() + 0.08 # Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Positive Prediction",fontsize=30) | |
| plt.ylabel("Feature", fontsize=28) | |
| plt.xlabel("Shap Value",fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.1, max([bar.get_width() for bar in bars]) + 0.1) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return plot | |
| # Plotting for negative class predictions (class label 0) | |
| else: | |
| neg_shap_values = shap_values[0][0] | |
| scores_desc = list(zip(neg_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] > 0 else blue for s in scores_desc] | |
| plot = plt.figure(tight_layout=True, figsize=(18,18)) | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.08 if score[0] < 0 else bar.get_width() + 0.08# Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Negative Prediction", fontsize=30) | |
| plt.ylabel("Feature",fontsize=28 ) | |
| plt.xlabel("Shap Value",fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.1, max([bar.get_width() for bar in bars]) + 0.1) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return plot | |
| # %% | |
| def update_data(example_df, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio): | |
| example_data = example_df | |
| example_data['Lounge_night_illuminance'] = Lounge_night_illuminance | |
| example_data['Kitchen_night_illuminance'] = Kitchen_night_illuminance | |
| example_data['Kitchen_afternoon_illuminance'] = Kitchen_afternoon_illuminance | |
| example_data['ratio'] = ratio | |
| example_data['Bathroom_night_temperature'] = Bathroom_night_temperature | |
| example_data['Bathroom_evening_temperature'] = Bathroom_evening_temperature | |
| example_data['Kitchen_night_temperature'] = Kitchen_night_temperature | |
| example_data['temperature_ratio'] = temperature_ratio | |
| example_df_norm = normalize(example_data, mean_df, std_df, top_features) | |
| df = example_df_norm[top_features] | |
| pos_pred = model.predict(df) | |
| proba = model.predict_proba(df) | |
| label = {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])} | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| df = example_df_norm[top_features] | |
| pos_pred = model.predict(df) | |
| proba = model.predict_proba(df) | |
| overall_prediction = "positive" if pos_pred[0] == 1 else "negative" | |
| class_index = 1 if overall_prediction == "positive" else 0 | |
| shap_values = explainer.shap_values(df) | |
| class_shap_values = shap_values[class_index][0] | |
| all_class_features = zip(class_shap_values, top_features, feature_values) | |
| sorted_class_features = sorted(all_class_features, key=lambda x: (x[0]), reverse=True) | |
| top_three_class_features = sorted_class_features[:3] | |
| statements = [] | |
| if proba[0][1] > 0.7940: | |
| statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:") | |
| if 0.3437 < proba[0][1] <= 0.7940: | |
| statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:") | |
| if proba[0][1] <= 0.3437: | |
| statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:") | |
| for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features): | |
| if feature_value > 0: | |
| feature_direction = "positive" | |
| else: | |
| feature_direction = "negative" | |
| # Additional information based on the feature and its direction | |
| if feature == "ratio": | |
| if feature_direction == "positive": | |
| statement= "The environment had a higher illuminance ratio (better light quality)." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment had a lower illuminance ratio (worse light quality, perhaps accompanied by glare)." | |
| statements.append(statement) | |
| elif feature == "awake_ratio": | |
| if feature_direction == "positive": | |
| statement= "The patient was alert during their sleep." | |
| statements.append(statement) | |
| else: | |
| statement= "The patient had lower levels of alertness during their sleep." | |
| statements.append(statement) | |
| elif feature == "rr_average": | |
| if feature_direction == "positive": | |
| statement= "The average respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The average respiratory rate during sleep was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "Lounge_night_illuminance": | |
| if feature_direction == "positive": | |
| statement= "There was higher illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| else: | |
| statement= "There was lower illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| elif feature == "Bathroom_evening_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the evening was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the evening was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_afternoon_illuminance": | |
| if feature_direction == "positive": | |
| statement = "The indoor light exposure in the kitchen in the afternoon was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The indoor light exposure in the kitchen in the afternoon was lower than usual." | |
| statements.append(statement) | |
| elif feature == "number_transitions": | |
| if feature_direction == "positive": | |
| statement= "The patient had increased movements from room to room in the house." | |
| statements.append(statement) | |
| else: | |
| statement="The patient tended to stay within specific rooms and did not move a lot from room to room." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| elif feature == "Lounge_afternoon_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement="The indoor light exposure in the kitchen at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The indoor light exposure in the kitchen at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement="The temperature in the bathroom at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min": | |
| if feature_direction == "positive": | |
| statement="The minimum respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The minimum respiratory rate was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "visibility": | |
| if feature_direction == "positive": | |
| statement= "The sky visibility was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The sky visibility was lower than usual." | |
| statements.append(statement) | |
| elif feature == "sunset_minutes": | |
| if feature_direction == "positive": | |
| statement= "The time of sunset was later than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The time of sunset was earlier than usual." | |
| statements.append(statement) | |
| elif feature == "uvindex": | |
| if feature_direction == "positive": | |
| statement= "The UV index was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The UV index was lower than usual." | |
| statements.append(statement) | |
| elif feature == "illuminance": | |
| if feature_direction == "positive": | |
| statement="Outdoor illuminance was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statements="Outdoor illuminance was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement = "There was higher illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| else: | |
| statement="There was lower illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| elif feature == "temperature_ratio": | |
| if feature_direction == "positive": | |
| statement = "The environment has a smaller difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment has a bigger difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min_std": | |
| if feature_direction == "positive": | |
| statement = "The variability in the minimum respiratory rate through the week was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The variability in the minimum respiratory rate through the week was lower than usual" | |
| statements.append(statement) | |
| elif feature == "To_bed": | |
| if feature_direction == "positive": | |
| statement = "The bedtimes of the participant during the week were more inconsistent than usual" | |
| statements.append(statement) | |
| else: | |
| statement= "The bedtimes of the participant during the week were more consistent than usual" | |
| statements.append(statement) | |
| html_output = "<br>".join(statements) | |
| formatted_statements = gr.HTML(html_output) | |
| # Select top features | |
| df = example_df_norm[top_features] | |
| # Make predictions | |
| predictions = model.predict(df) | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| # Calculate SHAP values for all data points | |
| shap_values = explainer.shap_values(df) | |
| base = explainer.expected_value | |
| # Plotting for positive class predictions (class label 1) | |
| if predictions[0] == 1: | |
| pos_shap_values = shap_values[1][0] | |
| scores_desc = list(zip(pos_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] < 0 else blue for s in scores_desc] | |
| plot = plt.figure(figsize=(18, 18)) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.015 if score[0] < 0 else bar.get_width() + 0.015 # Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Positive Prediction",fontsize=30) | |
| plt.ylabel("Feature", fontsize=28) | |
| plt.xlabel("SHAP Value", fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.03, max([bar.get_width() for bar in bars]) + 0.03) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])},formatted_statements, plot | |
| # Plotting for negative class predictions (class label 0) | |
| else: | |
| neg_shap_values = shap_values[0][0] | |
| scores_desc = list(zip(neg_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] > 0 else blue for s in scores_desc] | |
| plot = plt.figure(tight_layout=True,figsize=(18, 18)) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.015 if score[0] < 0 else bar.get_width() + 0.015 # Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Negative Prediction",fontsize=30) | |
| plt.ylabel("Feature", fontsize=28) | |
| plt.xlabel("SHAP Value", fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.03, max([bar.get_width() for bar in bars]) + 0.03) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])},formatted_statements, plot | |
| # %% | |
| import pandas as pd | |
| import numpy as np | |
| def generate_data(): | |
| data = np.random.rand(1, len(top_features)) | |
| df = pd.DataFrame(data, columns=top_features) | |
| # Assign specific values for each column | |
| df['sunset_minutes'] = 900.015348 | |
| df['cloudcover'] = 41 | |
| df['visibility'] = 16 | |
| df['illuminance'] = 8000 | |
| df['UV_index'] = 2 | |
| df['rr_minimum'] = np.random.uniform(8, 18) | |
| df['rr_average'] = np.random.uniform(8, 18) | |
| df['awake_ratio'] = np.random.uniform(0, 1)# Random value between 0 and 40 | |
| df['Lounge_night_illuminance'] = np.random.uniform(0, 500) | |
| df['Kitchen_night_illuminance'] = np.random.uniform(0, 500) | |
| df['Kitchen_afternoon_illuminance'] = np.random.uniform(0, 5000) | |
| df['ratio'] = np.random.uniform(0, 15) | |
| df['temperature_ratio'] = np.random.uniform(-1.5, 1.5) | |
| df['Bathroom_night_temperature'] = np.random.uniform(0, 40) # Random value between 0 and 40 | |
| df['Bathroom_evening_temperature'] = np.random.uniform(0, 40) | |
| df['Kitchen_night_temperature'] = np.random.uniform(0, 40) | |
| df['number_transitions'] = np.random.uniform(0,500) | |
| df['rr_min_std'] = np.random.uniform(0,1) | |
| df['To_bed'] = np.random.uniform(50,360) | |
| # Retrieve the values | |
| Kitchen_afternoon_illuminance = df['Kitchen_afternoon_illuminance'].astype(float).iloc[-1] | |
| Kitchen_night_illuminance = df['Kitchen_night_illuminance'].astype(float).iloc[-1] | |
| Lounge_night_illuminance = df['Lounge_night_illuminance'].astype(float).iloc[-1] | |
| ratio = df['ratio'].astype(float).iloc[-1] | |
| Kitchen_night_temperature= df['Kitchen_night_temperature'].astype(float).iloc[-1] | |
| Bathroom_night_temperature= df['Bathroom_night_temperature'].astype(float).iloc[-1] | |
| Bathroom_evening_temperature= df['Bathroom_evening_temperature'].astype(float).iloc[-1] | |
| temperature_ratio = df['temperature_ratio'].astype(float).iloc[-1] | |
| return df,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| # %% | |
| import pandas as pd | |
| import numpy as np | |
| def return_original(example_df): | |
| example_data = example_df | |
| Lounge_night_illuminance = example_data['Lounge_night_illuminance'].astype(float).iloc[-1] | |
| Kitchen_night_illuminance = example_data['Kitchen_night_illuminance'].astype(float).iloc[-1] | |
| Kitchen_afternoon_illuminance = example_data['Kitchen_afternoon_illuminance'].astype(float).iloc[-1] | |
| ratio = example_data['ratio'].astype(float).iloc[-1] | |
| temperature_ratio = example_data['temperature_ratio'].astype(float).iloc[-1] | |
| Bathroom_night_temperature= example_data['Bathroom_night_temperature'].astype(float).iloc[-1] | |
| Bathroom_evening_temperature= example_data['Bathroom_evening_temperature'].astype(float).iloc[-1] | |
| Kitchen_night_temperature= example_data['Kitchen_night_temperature'].astype(float).iloc[-1] | |
| example_df_norm = normalize(example_data, mean_df, std_df, top_features) | |
| df = example_df_norm[top_features] | |
| pos_pred = model.predict(df) | |
| proba = model.predict_proba(df) | |
| # Normalize the example data | |
| label = {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])} | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| df = example_df_norm[top_features] | |
| pos_pred = model.predict(df) | |
| proba = model.predict_proba(df) | |
| overall_prediction = "positive" if pos_pred[0] == 1 else "negative" | |
| class_index = 1 if overall_prediction == "positive" else 0 | |
| shap_values = explainer.shap_values(df) | |
| class_shap_values = shap_values[class_index][0] | |
| all_class_features = zip(class_shap_values, top_features, feature_values) | |
| sorted_class_features = sorted(all_class_features, key=lambda x: (x[0]), reverse=True) | |
| top_three_class_features = sorted_class_features[:3] | |
| statements = [] | |
| if proba[0][1] > 0.7940: | |
| statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:") | |
| if 0.3437 < proba[0][1] <= 0.7940: | |
| statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:") | |
| if proba[0][1] <= 0.3437: | |
| statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:") | |
| for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features): | |
| if feature_value > 0: | |
| feature_direction = "positive" | |
| else: | |
| feature_direction = "negative" | |
| if feature == "ratio": | |
| if feature_direction == "positive": | |
| statement= "The environment had a higher illuminance ratio (better light quality)." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment had a lower illuminance ratio (worse light quality, perhaps accompanied by glare)." | |
| statements.append(statement) | |
| elif feature == "awake_ratio": | |
| if feature_direction == "positive": | |
| statement= "The patient was alert during their sleep." | |
| statements.append(statement) | |
| else: | |
| statement= "The patient had lower levels of alertness during their sleep." | |
| statements.append(statement) | |
| elif feature == "rr_average": | |
| if feature_direction == "positive": | |
| statement= "The average respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The average respiratory rate during sleep was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "Lounge_night_illuminance": | |
| if feature_direction == "positive": | |
| statement= "There was higher illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| else: | |
| statement= "There was lower illuminance than usual observed in the lounge at night." | |
| statements.append(statement) | |
| elif feature == "Bathroom_evening_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the evening was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the evening was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement= "The temperature in the bathroom in the night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom in the night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_afternoon_illuminance": | |
| if feature_direction == "positive": | |
| statement = "The indoor light exposure in the kitchen in the afternoon was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The indoor light exposure in the kitchen in the afternoon was lower than usual." | |
| statements.append(statement) | |
| elif feature == "number_transitions": | |
| if feature_direction == "positive": | |
| statement= "The patient had increased movements from room to room in the house." | |
| statements.append(statement) | |
| else: | |
| statement="The patient tended to stay within specific rooms and did not move a lot from room to room." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the bathroom in the night." | |
| statements.append(statement) | |
| elif feature == "Lounge_afternoon_activity": | |
| if feature_direction == "positive": | |
| statement="More time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| else: | |
| statement="Less time than usual was spent in the lounge in the afternoon." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement="The indoor light exposure in the kitchen at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The indoor light exposure in the kitchen at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Bathroom_night_temperature": | |
| if feature_direction == "positive": | |
| statement="The temperature in the bathroom at night was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The temperature in the bathroom at night was lower than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min": | |
| if feature_direction == "positive": | |
| statement="The minimum respiratory rate during sleep was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The minimum respiratory rate was lower than usual. Perhaps the patient was experiencing breathing disruptions." | |
| statements.append(statement) | |
| elif feature == "visibility": | |
| if feature_direction == "positive": | |
| statement= "The sky visibility was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The sky visibility was lower than usual." | |
| statements.append(statement) | |
| elif feature == "sunset_minutes": | |
| if feature_direction == "positive": | |
| statement= "The time of sunset was later than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The time of sunset was earlier than usual." | |
| statements.append(statement) | |
| elif feature == "uvindex": | |
| if feature_direction == "positive": | |
| statement= "The UV index was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement="The UV index was lower than usual." | |
| statements.append(statement) | |
| elif feature == "illuminance": | |
| if feature_direction == "positive": | |
| statement="Outdoor illuminance was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statements="Outdoor illuminance was lower than usual." | |
| statements.append(statement) | |
| elif feature == "Kitchen_night_illuminance": | |
| if feature_direction == "positive": | |
| statement = "There was higher illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| else: | |
| statement="There was lower illuminance than usual observed in the kitchen at night." | |
| statements.append(statement) | |
| elif feature == "temperature_ratio": | |
| if feature_direction == "positive": | |
| statement = "The environment a smaller difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The environment a bigger difference between indoor and outdoor temperature than usual." | |
| statements.append(statement) | |
| elif feature == "rr_min_std": | |
| if feature_direction == "positive": | |
| statement = "The variability in the minimum respiratory rate through the week was higher than usual." | |
| statements.append(statement) | |
| else: | |
| statement= "The variability in the minimum respiratory rate through the week was lower than usual" | |
| statements.append(statement) | |
| elif feature == "To_bed": | |
| if feature_direction == "positive": | |
| statement = "The bedtimes of the participant during the week were more inconsistent than usual" | |
| statements.append(statement) | |
| else: | |
| statement= "The bedtimes of the participant during the week were more consistent than usual" | |
| statements.append(statement) | |
| html_output = "<br>".join(statements) | |
| formatted_statements = gr.HTML(html_output) | |
| # Select top features | |
| df = example_df_norm[top_features] | |
| # Make predictions | |
| predictions = model.predict(df) | |
| feature_values = example_df_norm[top_features].iloc[0] | |
| # Calculate SHAP values for all data points | |
| shap_values = explainer.shap_values(df) | |
| base = explainer.expected_value | |
| # Plotting for positive class predictions (class label 1) | |
| if predictions[0] == 1: | |
| pos_shap_values = shap_values[1][0] | |
| scores_desc = list(zip(pos_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] < 0 else blue for s in scores_desc] | |
| plot = plt.figure(tight_layout=True, figsize=(18,18)) | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.08 if score[0] < 0 else bar.get_width() + 0.08 # Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Positive Prediction",fontsize=30) | |
| plt.ylabel("Feature", fontsize=28) | |
| plt.xlabel("Shap Value",fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.1, max([bar.get_width() for bar in bars]) + 0.1) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])}, formatted_statements, plot,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| # Plotting for negative class predictions (class label 0) | |
| else: | |
| neg_shap_values = shap_values[0][0] | |
| scores_desc = list(zip(neg_shap_values, top_features, feature_values)) | |
| scores_desc = sorted(scores_desc) | |
| colors = [red if s[0] > 0 else blue for s in scores_desc] | |
| plot = plt.figure(tight_layout=True, figsize=(18,18)) | |
| plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| bars = plt.barh([plot_feature_names.get(s[1], s[1]) for s in scores_desc], [s[0] for s in scores_desc], color=colors) | |
| for bar, score in zip(bars, scores_desc): | |
| text_color = 'black' | |
| text_value = f'{score[2]:.2f}' | |
| text_position = bar.get_width() - 0.08 if score[0] < 0 else bar.get_width() + 0.08# Adjust the padding as needed | |
| plt.text(text_position, bar.get_y() + bar.get_height() / 2, text_value, ha='left' if score[0] < 0 else 'right', va='center', color=text_color, fontsize=20) | |
| plt.title("Feature Shap Values for Negative Prediction", fontsize=30) | |
| plt.ylabel("Feature",fontsize=28 ) | |
| plt.xlabel("Shap Value",fontsize=28) | |
| plt.xlim(min([bar.get_width() for bar in bars]) - 0.1, max([bar.get_width() for bar in bars]) + 0.1) | |
| plt.xticks(fontsize=28) | |
| plt.yticks(fontsize=28) | |
| plt.tight_layout() | |
| return {"Agitated": float(proba[0][1]), "Non-agitated": float(proba[0][0])}, formatted_statements, plot, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| def save_file(example_df,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio): | |
| example_data = example_df | |
| example_data['Lounge_night_illuminance'] = Lounge_night_illuminance | |
| example_data['Kitchen_night_illuminance'] = Kitchen_night_illuminance | |
| example_data['Kitchen_afternoon_illuminance'] = Kitchen_afternoon_illuminance | |
| example_data['ratio'] = ratio | |
| example_data['temperature_ratio'] = temperature_ratio | |
| example_data['Bathroom_night_temperature'] = Bathroom_night_temperature | |
| example_data['Bathroom_evening_temperature'] = Bathroom_evening_temperature | |
| example_data['Kitchen_night_temperature'] = Kitchen_night_temperature | |
| current_path = os.getcwd() | |
| output_filename = current_path + "Downloads/Data_modified.csv" | |
| example_data.to_csv(output_filename, index=False) | |
| return output_filename | |
| explainer =shap.TreeExplainer(model) | |
| # %% | |
| with gr.Blocks(theme=gr.themes.Default(spacing_size="md", primary_hue="red", secondary_hue="blue")) as demo: | |
| gr.HTML(""" | |
| <div style="margin-top: 20px; text-align: center;"> | |
| <h1 style="font-size: 36px; font-weight: bold; color: #377eb8;"> | |
| Enhancing Dementia Care: An Interpretable Machine Learning Tool for In-Home Screening of Agitation Episodes | |
| </h1> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| generate = gr.Button(value="Generate synthetic patient data") | |
| csv_file = gr.DataFrame(headers=['ratio', 'sunset_minutes', 'Kitchen_night_temperature', 'uvindex', 'visibility','illuminance','Kitchen_night_illuminance','Lounge_night_illuminance','Bathroom_night_temperature','Bathroom_evening_temperature', 'Bathroom_night_activity', 'number_transitions', 'temperature_ratio', 'awake_ratio','rr_min','To_bed', 'Lounge_afternoon_activity','rr_average',"rr_min_std",'Kitchen_afternoon_illuminance'],row_count=1 , col_count=20, datatype = "number", interactive=False) | |
| modif = gr.Button(value="Change the modifiable parameters to see how agitation risk changes") | |
| gr.Markdown("""<p style="margin-top: 20px; font-size: 16px; font-weight: bold;"> Light exposure </p>""") | |
| Kitchen_afternoon_illuminance = gr.Slider(label="Mean afternoon illuminance in kitchen", minimum=0, maximum=3000, step=100) | |
| Kitchen_night_illuminance = gr.Slider(label="Mean night illuminance in kitchen", minimum=0, maximum=3000, step=100) | |
| Lounge_night_illuminance = gr.Slider(label="Mean night illuminance in lounge", minimum=0, maximum=3000, step=100, every=True) | |
| ratio = gr.Slider(label="Ratio (indoor:outdoor illuminance)", minimum=0, maximum=0.35, step=0.1) | |
| gr.Markdown("""<p style="margin-top: 20px; font-size: 16px; font-weight: bold;"> Ambient temperature </p>""") | |
| Bathroom_night_temperature = gr.Slider(label="Mean night temperature in bathroom", minimum=-10, maximum=45, step=0.5) | |
| Bathroom_evening_temperature = gr.Slider(label="Mean evening temperature in bathroom", minimum=-10, maximum=45, step=0.5) | |
| Kitchen_night_temperature = gr.Slider(label="Mean night temperature in kitchen", minimum=-10, maximum=45, step=0.5) | |
| temperature_ratio = gr.Slider(label="Ratio (indoor:outdoor temperature)", minimum=-500, maximum=60, step=0.1) | |
| with gr.Row(): | |
| initialise = gr.Button(value="Return to original patient data") | |
| flag = gr.Button(value="Save these combinations of parameters" ,variant='primary') | |
| with gr.Column(): | |
| predict_btn = gr.Button(value="Screen patient for agitation") | |
| label = gr.Label(scale= 0, label="Patient screening") | |
| html_text = gr.HTML(label="Explanation") | |
| interpret_btn = gr.Button(value="Show each feature's contribution for this prediction") | |
| plot = gr.Plot(scale=2, label="Interpretation") | |
| with gr.Row(): | |
| generate.click(generate_data, outputs=[csv_file,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio]) | |
| Lounge_night_illuminance.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text, plot]) | |
| Kitchen_night_illuminance.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text, plot]) | |
| Kitchen_afternoon_illuminance.input(update_data, inputs=[csv_file,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| Bathroom_night_temperature.input(update_data, inputs=[csv_file,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| ratio.input(update_data, inputs=[csv_file,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| temperature_ratio.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| Bathroom_night_temperature.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| Bathroom_evening_temperature.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| Kitchen_night_temperature.input(update_data, inputs=[csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], outputs= [label,html_text,plot]) | |
| predict_btn.click( | |
| predict, | |
| inputs=[ | |
| csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| ], | |
| outputs=[label,html_text] | |
| ) | |
| interpret_btn.click( | |
| interpret, | |
| inputs=[ | |
| csv_file, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio | |
| ], | |
| outputs=[plot], | |
| ) | |
| initialise.click( | |
| return_original, | |
| inputs=[ | |
| csv_file | |
| ], | |
| outputs=[label,html_text, plot, Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], | |
| ) | |
| flag.click( | |
| save_file, | |
| inputs= [csv_file,Kitchen_afternoon_illuminance, Lounge_night_illuminance, Kitchen_night_illuminance, ratio, Kitchen_night_temperature, Bathroom_night_temperature, Bathroom_evening_temperature, temperature_ratio], | |
| ) | |
| with gr.Row(): | |
| gr.Markdown("<img src='https://ukdri.ac.uk/assets/img/logo.svg' alt='UKDRI' width='250' style='margin-top: 50px;'>") | |
| gr.Markdown("<img src='https://ukdri.ac.uk/uploads/New-MRC-website.jpg' alt='MRC_FOUNDING' width='150' style='margin-top: 20px;'>") | |
| gr.Markdown("<img src='https://ukdri.ac.uk/uploads/AS_LOGO_NEW_WEBSITE.jpg' alt='ALZHEIMERSSOCIETY_FOUNDING' width='150' style='margin-top: 20px;'>") | |
| gr.Markdown("<img src='https://ukdri.ac.uk/uploads/New-ARUK-website-global.jpg' alt='Explain Image' width='150' style='margin-top: 25px;'>") | |
| demo.launch(auth=(os.environ['user'], os.environ['password'])) | |
| # %% | |