Refactor machine_learning/pipeline.py by defining one class by file.

exploration/ex_ml_pipeline.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.4
+#       jupytext_version: 1.12.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -32,6 +32,9 @@ if nb_dir not in sys.path:
 # %%
 import participants.query_db
 from features import esm, helper, proximity
+import machine_learning.features_sensor
+import machine_learning.labels
+import machine_learning.model
 
 # %% [markdown]
 # # 1. Get the relevant data
@@ -166,7 +169,7 @@ with open("../machine_learning/config/minimal_features.yaml", "r") as file:
 print(sensor_features_params)
 
 # %%
-sensor_features = pipeline.SensorFeatures(**sensor_features_params)
+sensor_features = machine_learning.features_sensor.SensorFeatures(**sensor_features_params)
 sensor_features.data_types
 
 # %%
@@ -188,12 +191,6 @@ sensor_features.get_sensor_data("proximity")
 # %%
 sensor_features.calculate_features()
 
-# %%
-sensor_features.get_features("proximity", "all")
-
-# %%
-sensor_features.get_features("communication", "all")
-
 # %%
 sensor_features.get_features("all", "all")
 
@@ -202,10 +199,16 @@ with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
     labels_params = yaml.safe_load(file)
 
 # %%
-labels = pipeline.Labels(**labels_params)
+labels = machine_learning.labels.Labels(**labels_params)
 labels.participants_usernames = ptcp_2
 labels.questionnaires
 
+# %%
+all_features = sensor_features.get_features("all", "all")
+
+# %%
+all_features.isna().any().any()
+
 # %%
 labels.set_labels()
 
@@ -219,7 +222,7 @@ labels.aggregate_labels()
 labels.get_aggregated_labels()
 
 # %%
-model_validation = pipeline.ModelValidation(
+model_validation = machine_learning.model.ModelValidation(
     sensor_features.get_features("all", "all"),
     labels.get_aggregated_labels(),
     group_variable="participant_id",

machine_learning/config/minimal_labels.yaml

@@ -1,4 +1,4 @@
-grouping_variable: [date_lj]
+grouping_variable: date_lj
 labels:
   PANAS:
     - PA

machine_learning/config/prox_comm_PANAS_features.yaml

@@ -1,4 +1,4 @@
-grouping_variable: [date_lj]
+grouping_variable: date_lj
 features:
   proximity:
     all

machine_learning/config/prox_comm_PANAS_labels.yaml

@@ -1,4 +1,4 @@
-grouping_variable: [date_lj]
+grouping_variable: date_lj
 labels:
   PANAS:
     - PA

machine_learning/features_sensor.py

@@ -0,0 +1,173 @@
+import datetime
+import warnings
+from pathlib import Path
+from typing import Collection
+
+import pandas as pd
+from pyprojroot import here
+
+import participants.query_db
+from features import proximity, helper, communication
+
+WARNING_PARTICIPANTS_LABEL = (
+    "Before calculating features, please set participants label using self.set_participants_label() "
+    "to be used as a filename prefix when exporting data. "
+    "The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
+)
+
+
+class SensorFeatures:
+    def __init__(
+        self,
+        grouping_variable: str,
+        features: dict,
+        participants_usernames: Collection = None,
+    ):
+
+        self.grouping_variable_name = grouping_variable
+        self.grouping_variable = [grouping_variable]
+
+        self.data_types = features.keys()
+
+        self.participants_label: str = ""
+        if participants_usernames is None:
+            participants_usernames = participants.query_db.get_usernames(
+                collection_start=datetime.date.fromisoformat("2020-08-01")
+            )
+            self.participants_label = "all"
+        self.participants_usernames = participants_usernames
+
+        self.df_features_all = pd.DataFrame()
+
+        self.df_proximity = pd.DataFrame()
+        self.df_proximity_counts = pd.DataFrame()
+
+        self.df_calls = pd.DataFrame()
+        self.df_sms = pd.DataFrame()
+        self.df_calls_sms = pd.DataFrame()
+
+        self.folder = None
+        self.filename_prefix = ""
+        self.construct_export_path()
+        print("SensorFeatures initialized.")
+
+    def set_sensor_data(self):
+        print("Querying database ...")
+        if "proximity" in self.data_types:
+            self.df_proximity = proximity.get_proximity_data(
+                self.participants_usernames
+            )
+            print("Got proximity data from the DB.")
+            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
+            self.df_proximity = proximity.recode_proximity(self.df_proximity)
+        if "communication" in self.data_types:
+            self.df_calls = communication.get_call_data(self.participants_usernames)
+            self.df_calls = helper.get_date_from_timestamp(self.df_calls)
+            print("Got calls data from the DB.")
+
+            self.df_sms = communication.get_sms_data(self.participants_usernames)
+            self.df_sms = helper.get_date_from_timestamp(self.df_sms)
+            print("Got sms data from the DB.")
+
+    def get_sensor_data(self, data_type) -> pd.DataFrame:
+        if data_type == "proximity":
+            return self.df_proximity
+        elif data_type == "communication":
+            return self.df_calls_sms
+        else:
+            raise KeyError("This data type has not been implemented.")
+
+    def calculate_features(self):
+        print("Calculating features ...")
+        if not self.participants_label:
+            raise ValueError(WARNING_PARTICIPANTS_LABEL)
+        if "proximity" in self.data_types:
+            self.df_proximity_counts = proximity.count_proximity(
+                self.df_proximity, self.grouping_variable
+            )
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_proximity_counts
+            )
+            print("Calculated proximity features.")
+        to_csv_with_settings(
+            self.df_proximity, self.folder, self.filename_prefix, data_type="prox"
+        )
+
+        if "communication" in self.data_types:
+            self.df_calls_sms = communication.calls_sms_features(
+                df_calls=self.df_calls,
+                df_sms=self.df_sms,
+                group_by=self.grouping_variable,
+            )
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_calls_sms
+            )
+            print("Calculated communication features.")
+        to_csv_with_settings(
+            self.df_calls_sms, self.folder, self.filename_prefix, data_type="comm"
+        )
+
+        self.df_features_all.fillna(
+            value=proximity.FILL_NA_PROXIMITY, inplace=True, downcast="infer",
+        )
+        self.df_features_all.fillna(
+            value=communication.FILL_NA_CALLS_SMS_ALL, inplace=True, downcast="infer",
+        )
+
+    def get_features(self, data_type, feature_names) -> pd.DataFrame:
+        if data_type == "proximity":
+            if feature_names == "all":
+                feature_names = proximity.FEATURES_PROXIMITY
+            return self.df_proximity_counts[feature_names]
+        elif data_type == "communication":
+            if feature_names == "all":
+                feature_names = communication.FEATURES_CALLS_SMS_ALL
+            return self.df_calls_sms[feature_names]
+        elif data_type == "all":
+            return self.df_features_all
+        else:
+            raise KeyError("This data type has not been implemented.")
+
+    def construct_export_path(self):
+        if not self.participants_label:
+            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
+        self.folder = here("machine_learning/intermediate_results/features", warn=True)
+        self.filename_prefix = (
+            self.participants_label + "_" + self.grouping_variable_name
+        )
+
+    def set_participants_label(self, label: str):
+        self.participants_label = label
+        self.construct_export_path()
+
+
+def safe_outer_merge_on_index(left, right):
+    if left.empty:
+        return right
+    elif right.empty:
+        return left
+    else:
+        return pd.merge(
+            left,
+            right,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            validate="one_to_one",
+        )
+
+
+def to_csv_with_settings(
+    df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
+) -> None:
+    export_filename = filename_prefix + "_" + data_type + ".csv"
+    full_path = folder / export_filename
+    df.to_csv(
+        path_or_buf=full_path,
+        sep=",",
+        na_rep="NA",
+        header=True,
+        index=False,
+        encoding="utf-8",
+    )
+    print("Exported the dataframe to " + str(full_path))

machine_learning/labels.py

@@ -0,0 +1,86 @@
+import datetime
+from typing import Collection
+
+import pandas as pd
+
+import participants.query_db
+from features import esm
+from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
+
+
+class Labels:
+    def __init__(
+        self,
+        grouping_variable: str,
+        labels: dict,
+        participants_usernames: Collection = None,
+    ):
+        self.grouping_variable_name = grouping_variable
+        self.grouping_variable = [grouping_variable]
+
+        self.questionnaires = labels.keys()
+
+        if participants_usernames is None:
+            participants_usernames = participants.query_db.get_usernames(
+                collection_start=datetime.date.fromisoformat("2020-08-01")
+            )
+        self.participants_usernames = participants_usernames
+
+        self.df_esm = pd.DataFrame()
+        self.df_esm_preprocessed = pd.DataFrame()
+        self.df_esm_interest = pd.DataFrame()
+        self.df_esm_clean = pd.DataFrame()
+
+        self.df_esm_means = pd.DataFrame()
+        print("Labels initialized.")
+
+    def set_labels(self):
+        print("Querying database ...")
+        self.df_esm = esm.get_esm_data(self.participants_usernames)
+        print("Got ESM data from the DB.")
+        self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
+        print("ESM data preprocessed.")
+        if "PANAS" in self.questionnaires:
+            self.df_esm_interest = self.df_esm_preprocessed[
+                (
+                    self.df_esm_preprocessed["questionnaire_id"]
+                    == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
+                )
+                | (
+                    self.df_esm_preprocessed["questionnaire_id"]
+                    == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
+                )
+            ]
+        self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
+        print("ESM data cleaned.")
+
+    def get_labels(self, questionnaire):
+        if questionnaire == "PANAS":
+            return self.df_esm_clean
+        else:
+            raise KeyError("This questionnaire has not been implemented as a label.")
+
+    def aggregate_labels(self):
+        print("Aggregating labels ...")
+        self.df_esm_means = (
+            self.df_esm_clean.groupby(
+                ["participant_id", "questionnaire_id"] + self.grouping_variable
+            )
+            .esm_user_answer_numeric.agg("mean")
+            .reset_index()
+            .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
+        )
+        self.df_esm_means = (
+            self.df_esm_means.pivot(
+                index=["participant_id"] + self.grouping_variable,
+                columns="questionnaire_id",
+                values="esm_numeric_mean",
+            )
+            .reset_index(col_level=1)
+            .rename(columns=QUESTIONNAIRE_IDS_RENAME)
+            .set_index(["participant_id"] + self.grouping_variable)
+        )
+        print("Labels aggregated.")
+
+    def get_aggregated_labels(self):
+        return self.df_esm_means

machine_learning/model.py

@@ -0,0 +1,47 @@
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
+
+
+class ModelValidation:
+    def __init__(self, X, y, group_variable=None, cv_name="loso"):
+        self.model = None
+        self.cv = None
+
+        idx_common = X.index.intersection(y.index)
+        self.y = y.loc[idx_common, "NA"]
+        # TODO Handle the case of multiple labels.
+        self.X = X.loc[idx_common]
+        self.groups = self.y.index.get_level_values(group_variable)
+
+        self.cv_name = cv_name
+        print("ModelValidation initialized.")
+
+    def set_cv_method(self):
+        if self.cv_name == "loso":
+            self.cv = LeaveOneGroupOut()
+            self.cv.get_n_splits(X=self.X, y=self.y, groups=self.groups)
+        print("Validation method set.")
+
+    def cross_validate(self):
+        print("Running cross validation ...")
+        if self.model is None:
+            raise TypeError(
+                "Please, specify a machine learning model first, by setting the .model attribute. "
+                "E.g. self.model = sklearn.linear_model.LinearRegression()"
+            )
+        if self.cv is None:
+            raise TypeError(
+                "Please, specify a cross validation method first, by using set_cv_method() first."
+            )
+        if self.X.isna().any().any() or self.y.isna().any().any():
+            raise ValueError(
+                "NaNs were found in either X or y. Please, check your data before continuing."
+            )
+        return cross_val_score(
+            estimator=self.model,
+            X=self.X,
+            y=self.y,
+            groups=self.groups,
+            cv=self.cv,
+            n_jobs=-1,
+            scoring="r2",
+        )

machine_learning/pipeline.py

@@ -1,305 +1,10 @@
-import datetime
-import warnings
-from collections.abc import Collection
-from pathlib import Path
-
 import numpy as np
-import pandas as pd
 import yaml
-from pyprojroot import here
 from sklearn import linear_model
-from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
-
-import participants.query_db
-from features import communication, esm, helper, proximity
-from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
-
-WARNING_PARTICIPANTS_LABEL = (
-    "Before calculating features, please set participants label using self.set_participants_label() "
-    "to be used as a filename prefix when exporting data. "
-    "The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
-)
-
-
-class SensorFeatures:
-    def __init__(
-        self,
-        grouping_variable: str,
-        features: dict,
-        participants_usernames: Collection = None,
-    ):
-
-        self.grouping_variable_name = grouping_variable
-        self.grouping_variable = [grouping_variable]
-
-        self.data_types = features.keys()
-
-        self.participants_label: str = ""
-        if participants_usernames is None:
-            participants_usernames = participants.query_db.get_usernames(
-                collection_start=datetime.date.fromisoformat("2020-08-01")
-            )
-            self.participants_label = "all"
-        self.participants_usernames = participants_usernames
-
-        self.df_features_all = pd.DataFrame()
-
-        self.df_proximity = pd.DataFrame()
-        self.df_proximity_counts = pd.DataFrame()
-
-        self.df_calls = pd.DataFrame()
-        self.df_sms = pd.DataFrame()
-        self.df_calls_sms = pd.DataFrame()
-
-        self.folder = None
-        self.filename_prefix = ""
-        self.construct_export_path()
-        print("SensorFeatures initialized.")
-
-    def set_sensor_data(self):
-        print("Querying database ...")
-        if "proximity" in self.data_types:
-            self.df_proximity = proximity.get_proximity_data(
-                self.participants_usernames
-            )
-            print("Got proximity data from the DB.")
-            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
-            self.df_proximity = proximity.recode_proximity(self.df_proximity)
-        if "communication" in self.data_types:
-            self.df_calls = communication.get_call_data(self.participants_usernames)
-            self.df_calls = helper.get_date_from_timestamp(self.df_calls)
-            print("Got calls data from the DB.")
-
-            self.df_sms = communication.get_sms_data(self.participants_usernames)
-            self.df_sms = helper.get_date_from_timestamp(self.df_sms)
-            print("Got sms data from the DB.")
-
-    def get_sensor_data(self, data_type) -> pd.DataFrame:
-        if data_type == "proximity":
-            return self.df_proximity
-        elif data_type == "communication":
-            return self.df_calls_sms
-        else:
-            raise KeyError("This data type has not been implemented.")
-
-    def calculate_features(self):
-        print("Calculating features ...")
-        if not self.participants_label:
-            raise ValueError(WARNING_PARTICIPANTS_LABEL)
-        if "proximity" in self.data_types:
-            self.df_proximity_counts = proximity.count_proximity(
-                self.df_proximity, self.grouping_variable
-            )
-            self.df_features_all = safe_outer_merge_on_index(
-                self.df_features_all, self.df_proximity_counts
-            )
-            print("Calculated proximity features.")
-        to_csv_with_settings(
-            self.df_proximity, self.folder, self.filename_prefix, data_type="prox"
-        )
-
-        if "communication" in self.data_types:
-            self.df_calls_sms = communication.calls_sms_features(
-                df_calls=self.df_calls,
-                df_sms=self.df_sms,
-                group_by=self.grouping_variable,
-            )
-            self.df_features_all = safe_outer_merge_on_index(
-                self.df_features_all, self.df_calls_sms
-            )
-            print("Calculated communication features.")
-        to_csv_with_settings(
-            self.df_calls_sms, self.folder, self.filename_prefix, data_type="comm"
-        )
-
-        self.df_features_all.fillna(
-            value=proximity.FILL_NA_PROXIMITY, inplace=True, downcast="infer",
-        )
-        self.df_features_all.fillna(
-            value=communication.FILL_NA_CALLS_SMS_ALL, inplace=True, downcast="infer",
-        )
-
-    def get_features(self, data_type, feature_names) -> pd.DataFrame:
-        if data_type == "proximity":
-            if feature_names == "all":
-                feature_names = proximity.FEATURES_PROXIMITY
-            return self.df_proximity_counts[feature_names]
-        elif data_type == "communication":
-            if feature_names == "all":
-                feature_names = communication.FEATURES_CALLS_SMS_ALL
-            return self.df_calls_sms[feature_names]
-        elif data_type == "all":
-            return self.df_features_all
-        else:
-            raise KeyError("This data type has not been implemented.")
-
-    def construct_export_path(self):
-        if not self.participants_label:
-            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
-        self.folder = here("machine_learning/intermediate_results/features", warn=True)
-        self.filename_prefix = (
-            self.participants_label + "_" + self.grouping_variable_name
-        )
-
-    def set_participants_label(self, label: str):
-        self.participants_label = label
-        self.construct_export_path()
-
-
-class Labels:
-    def __init__(
-        self,
-        grouping_variable: list,
-        labels: dict,
-        participants_usernames: Collection = None,
-    ):
-        self.grouping_variable = grouping_variable
-
-        self.questionnaires = labels.keys()
-
-        if participants_usernames is None:
-            participants_usernames = participants.query_db.get_usernames(
-                collection_start=datetime.date.fromisoformat("2020-08-01")
-            )
-        self.participants_usernames = participants_usernames
-
-        self.df_esm = pd.DataFrame()
-        self.df_esm_preprocessed = pd.DataFrame()
-        self.df_esm_interest = pd.DataFrame()
-        self.df_esm_clean = pd.DataFrame()
-
-        self.df_esm_means = pd.DataFrame()
-        print("Labels initialized.")
-
-    def set_labels(self):
-        print("Querying database ...")
-        self.df_esm = esm.get_esm_data(self.participants_usernames)
-        print("Got ESM data from the DB.")
-        self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
-        print("ESM data preprocessed.")
-        if "PANAS" in self.questionnaires:
-            self.df_esm_interest = self.df_esm_preprocessed[
-                (
-                    self.df_esm_preprocessed["questionnaire_id"]
-                    == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
-                )
-                | (
-                    self.df_esm_preprocessed["questionnaire_id"]
-                    == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
-                )
-            ]
-        self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
-        print("ESM data cleaned.")
-
-    def get_labels(self, questionnaire):
-        if questionnaire == "PANAS":
-            return self.df_esm_clean
-        else:
-            raise KeyError("This questionnaire has not been implemented as a label.")
-
-    def aggregate_labels(self):
-        print("Aggregating labels ...")
-        self.df_esm_means = (
-            self.df_esm_clean.groupby(
-                ["participant_id", "questionnaire_id"] + self.grouping_variable
-            )
-            .esm_user_answer_numeric.agg("mean")
-            .reset_index()
-            .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
-        )
-        self.df_esm_means = (
-            self.df_esm_means.pivot(
-                index=["participant_id"] + self.grouping_variable,
-                columns="questionnaire_id",
-                values="esm_numeric_mean",
-            )
-            .reset_index(col_level=1)
-            .rename(columns=QUESTIONNAIRE_IDS_RENAME)
-            .set_index(["participant_id"] + self.grouping_variable)
-        )
-        print("Labels aggregated.")
-
-    def get_aggregated_labels(self):
-        return self.df_esm_means
-
-
-class ModelValidation:
-    def __init__(self, X, y, group_variable=None, cv_name="loso"):
-        self.model = None
-        self.cv = None
-
-        idx_common = X.index.intersection(y.index)
-        self.y = y.loc[idx_common, "NA"]
-        # TODO Handle the case of multiple labels.
-        self.X = X.loc[idx_common]
-        self.groups = self.y.index.get_level_values(group_variable)
-
-        self.cv_name = cv_name
-        print("ModelValidation initialized.")
-
-    def set_cv_method(self):
-        if self.cv_name == "loso":
-            self.cv = LeaveOneGroupOut()
-            self.cv.get_n_splits(X=self.X, y=self.y, groups=self.groups)
-        print("Validation method set.")
-
-    def cross_validate(self):
-        print("Running cross validation ...")
-        if self.model is None:
-            raise TypeError(
-                "Please, specify a machine learning model first, by setting the .model attribute. "
-                "E.g. self.model = sklearn.linear_model.LinearRegression()"
-            )
-        if self.cv is None:
-            raise TypeError(
-                "Please, specify a cross validation method first, by using set_cv_method() first."
-            )
-        if self.X.isna().any().any() or self.y.isna().any().any():
-            raise ValueError(
-                "NaNs were found in either X or y. Please, check your data before continuing."
-            )
-        return cross_val_score(
-            estimator=self.model,
-            X=self.X,
-            y=self.y,
-            groups=self.groups,
-            cv=self.cv,
-            n_jobs=-1,
-            scoring="r2",
-        )
-
-
-def safe_outer_merge_on_index(left, right):
-    if left.empty:
-        return right
-    elif right.empty:
-        return left
-    else:
-        return pd.merge(
-            left,
-            right,
-            how="outer",
-            left_index=True,
-            right_index=True,
-            validate="one_to_one",
-        )
-
-
-def to_csv_with_settings(
-    df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
-) -> None:
-    export_filename = filename_prefix + "_" + data_type + ".csv"
-    full_path = folder / export_filename
-    df.to_csv(
-        path_or_buf=full_path,
-        sep=",",
-        na_rep="NA",
-        header=True,
-        index=False,
-        encoding="utf-8",
-    )
-    print("Exported the dataframe to " + str(full_path))
 
+from machine_learning.features_sensor import SensorFeatures
+from machine_learning.labels import Labels
+from machine_learning.model import ModelValidation
 
 if __name__ == "__main__":
     with open("./config/prox_comm_PANAS_features.yaml", "r") as file: