From 8daa5ef20eb755d0b3d9f4fa7040c9b3455d959f Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 30 Nov 2020 15:43:03 -0500 Subject: [PATCH] Update add-new-features.md --- docs/features/add-new-features.md | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index b5c3019c..1b8a6071 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -26,12 +26,18 @@ As a tutorial, we will add a new provider for `PHONE_ACCELEROMETER` called `VEGA - Phone Bluetooth - Phone Calls - Phone Conversation + - Phone Data Yield - Phone Light - Phone Locations - Phone Messages - Phone Screen - Phone WiFI Connected - Phone WiFI Visible + - Fitbit Heart Rate Summary + - Fitbit Heart Rate Intraday + - Fitbit Sleep Summary + - Fitbit Steps Summary + - Fitbit Steps Intraday ### Modify the `config.yaml` file @@ -129,7 +135,7 @@ The code to extract your behavioral features should be implemented in your provi Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called `local_segment`. This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: ```python - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() ``` The reason RAPIDS does not filter the participant's data set for you is because your code might need to compute something based on a participant's complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number. @@ -139,7 +145,7 @@ The code to extract your behavioral features should be implemented in your provi - One row per day segment instance (e.g. 14 our `p01`'s `my_days` example) - The `local_segment` column added by `filter_data_by_segment()` - - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`accelerometr_vega_`) + - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`phone_accelerometr_vega_`) ??? example "`PHONE_ACCELEROMETER` Provider Example" For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features @@ -154,7 +160,7 @@ The code to extract your behavioral features should be implemented in your provi # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: acc_data = filter_data_by_segment(acc_data, day_segment) @@ -165,15 +171,15 @@ The code to extract your behavioral features should be implemented in your provi acc_data = acc_data.assign(magnitude = magnitude.values) if "maxmagnitude" in features_to_compute: - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() if "minmagnitude" in features_to_compute: - acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() if "avgmagnitude" in features_to_compute: - acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() if "medianmagnitude" in features_to_compute: - acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() if "stdmagnitude" in features_to_compute: - acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() acc_features = acc_features.reset_index()