Implement algorithm for sequential adding of the most important features.
Primoz
parent
85e572fca0
commit
07ef72dec5
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@ -72,7 +72,7 @@ demo_features = ['demo_age', 'demo_limesurvey_demand', 'demo_limesurvey_control'
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# %%
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# Get phone and non-phone columns
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def make_predictions_with_sensor_groups(df, groups_substrings, include_group=True):
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def make_predictions_with_sensor_groups(df, groups_substrings, include_group=True, with_cols=[]):
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"""
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This function makes predictions with sensor groups.
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It takes in a dataframe (df), a list of group substrings (groups_substrings)
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@ -87,6 +87,10 @@ def make_predictions_with_sensor_groups(df, groups_substrings, include_group=Tru
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is set to True or False.
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"""
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best_sensor = None
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best_recall_score, best_f1_sore = None, None
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for fgroup_substr in groups_substrings:
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if fgroup_substr is None:
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feature_group_cols = list(df.columns)
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@ -99,7 +103,10 @@ def make_predictions_with_sensor_groups(df, groups_substrings, include_group=Tru
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feature_group_cols = [col for col in df.columns if fgroup_substr not in col and col not in ['pid', 'target']]
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X, y = df.drop(columns=['target', 'pid'])[feature_group_cols], df['target']
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X, y = df.drop(columns=['target', 'pid'])[feature_group_cols+with_cols], df['target']
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print(fgroup_substr, X.shape)
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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X = imputer.fit_transform(X)
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@ -115,39 +122,78 @@ def make_predictions_with_sensor_groups(df, groups_substrings, include_group=Tru
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else:
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print("\nPrediction without", fgroup_substr)
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acc = metrics.accuracy_score(y_test, y_pred)
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prec = metrics.precision_score(y_test, y_pred)
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rec = metrics.recall_score(y_test, y_pred)
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f1 = metrics.f1_score(y_test, y_pred)
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print("************************************************")
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print("Accuracy", metrics.accuracy_score(y_test, y_pred))
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print("Precision", metrics.precision_score(y_test, y_pred))
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print("Recall", metrics.recall_score(y_test, y_pred))
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print("F1", metrics.f1_score(y_test, y_pred), "\n")
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print("Accuracy", acc)
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print("Precision", prec)
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print("Recall", rec)
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print("F1", f1, "\n")
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if (not best_recall_score and not best_f1_sore) or (rec > best_recall_score):
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best_sensor = fgroup_substr
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best_recall_score, best_f1_sore = rec, f1
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return best_sensor, best_recall_score, best_f1_sore
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# %% [markdown]
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# ### Senzor big feature groups (phone, empatica, demografical)
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groups_substr = [None, "phone_", "empatica_", "demo_"]
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=groups_substr, include_group=False)
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# ### sensor big feature groups (phone, empatica, demografical)
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big_groups_substr = ["phone_", "empatica_", "demo_"]
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=big_groups_substr, include_group=False)
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# %% [markdown]
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# ### Empatica sezor groups
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
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e4_sensors = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_"]
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=e4_sensors, include_group=False)
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# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
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# e4_sensors = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_"]
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# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=e4_sensors, include_group=False)
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# %% [markdown]
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# ### Phone sensor groups
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
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phone_sensors = ["phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery", "phone_calls_", "phone_data_yield_",
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"phone_light_", "phone_location_", "phone_messages", "phone_screen_", "phone_speech_"]
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make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=phone_sensors, include_group=False)
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# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
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# phone_sensors = ["phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery", "phone_calls_", "phone_data_yield_",
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# "phone_light_", "phone_location_", "phone_messages", "phone_screen_", "phone_speech_"]
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# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=phone_sensors, include_group=False)
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# %%
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# Write all the sensors (phone, empatica), seperate other (demographical) cols also
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sensors_features_groups = ["_", "phone_", "empatica_", "empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_",
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"phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery", "phone_calls_", "phone_data_yield_", "phone_light_"
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"phone_location_", "phone_messages", "phone_screen_", "phone_speech_"]
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sensors_features_groups = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_",
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"phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery_", "phone_calls_", "phone_data_yield_", "phone_light_",
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"phone_locations_", "phone_messages", "phone_screen_"] # , "phone_speech_"]
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# %%
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def find_sensor_group_features_importance(model_input, sensor_groups_strings):
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sensor_importance_scores = []
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model_input = model_input.copy()
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sensor_groups_strings = sensor_groups_strings.copy()
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groups_len = len(sensor_groups_strings)
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for i in range(groups_len):
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important_cols = [col[0] for col in sensor_importance_scores]
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with_cols = [col for col in model_input.columns if any(col.startswith(y) for y in important_cols)]
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best_sensor, best_recall_score, best_f1_sore = \
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make_predictions_with_sensor_groups(model_input,
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groups_substrings=sensor_groups_strings, include_group=True,
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with_cols=with_cols)
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sensor_importance_scores.append((best_sensor, best_recall_score, best_f1_sore))
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print(f"\nAdded sensor: {best_sensor}\n")
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sensor_groups_strings.remove(best_sensor)
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print([col for col in model_input.columns if "phone_" not in col or "empatica_" not in col])
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return sensor_importance_scores
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# sensor_importance_scores = find_sensor_group_features_importance(model_input, big_groups_substr)
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sensor_groups_importance_scores = find_sensor_group_features_importance(model_input, sensors_features_groups)
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# %%
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best_sensor_group = sensor_groups_importance_scores[0][0]
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best_sensor_features = [col for col in model_input if col.startswith(best_sensor_group)]
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best_sensor_group_importance_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
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# %%
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# TODO: visualization of sensor_group and best_sensor_group importance (Recall/f1-score chart)
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