Move to presentation.

.gitignore

@@ -5,6 +5,7 @@ __pycache__/
 /exploration/*.ipynb
 /config/*.ipynb
 /statistical_analysis/*.ipynb
+/presentation/*.ipynb
 /machine_learning/intermediate_results/
 /data/features/
 /data/baseline/

presentation/prox_comm_PANAS_nb.py

@@ -0,0 +1,131 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.13.0
+#   kernelspec:
+#     display_name: straw2analysis
+#     language: python
+#     name: straw2analysis
+# ---
+
+# %%
+# %matplotlib inline
+import yaml
+from sklearn import linear_model
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
+import os
+import importlib
+import matplotlib.pyplot as plt
+import sys
+import numpy as np
+import seaborn as sns
+import pandas as pd
+
+nb_dir = os.path.split(os.getcwd())[0]
+if nb_dir not in sys.path:
+    sys.path.append(nb_dir)
+
+# %%
+from machine_learning import pipeline, features_sensor, labels, model
+
+# %%
+importlib.reload(labels)
+
+# %%
+with open("./config/prox_comm_PANAS_features.yaml", "r") as file:
+    sensor_features_params = yaml.safe_load(file)
+sensor_features = features_sensor.SensorFeatures(**sensor_features_params)
+#sensor_features.set_sensor_data()
+sensor_features.calculate_features(cached=True)
+
+# %%
+all_features = sensor_features.get_features("all","all")
+
+# %%
+with open("./config/prox_comm_PANAS_labels.yaml", "r") as file:
+    labels_params = yaml.safe_load(file)
+labels_current = labels.Labels(**labels_params)
+#labels_current.set_labels()
+labels_current.aggregate_labels(cached=True)
+
+# %%
+model_validation = model.ModelValidation(
+    sensor_features.get_features("all", "all"),
+    labels_current.get_aggregated_labels(),
+    group_variable="participant_id",
+    cv_name="loso",
+)
+model_validation.model = linear_model.LinearRegression()
+model_validation.set_cv_method()
+
+# %%
+model_loso_r2 = model_validation.cross_validate()
+
+# %%
+print(model_loso_r2)
+print(np.mean(model_loso_r2))
+
+# %%
+model_loso_r2[model_loso_r2 > 0]
+
+# %%
+logo = LeaveOneGroupOut()
+
+# %%
+try_X = model_validation.X.reset_index().drop(["participant_id","date_lj"], axis=1)
+try_y = model_validation.y.reset_index().drop(["participant_id","date_lj"], axis=1)
+
+# %%
+model_loso_mean_absolute_error = -1 * cross_val_score(
+estimator=model_validation.model,
+X=try_X,
+y=try_y,
+groups=model_validation.groups,
+cv=logo.split(X=try_X, y=try_y, groups=model_validation.groups), 
+scoring='neg_mean_absolute_error'
+)
+
+# %%
+model_loso_mean_absolute_error
+
+# %%
+np.median(model_loso_mean_absolute_error)
+
+# %%
+model_validation.model.fit(try_X, try_y)
+
+# %%
+Y_predicted = model_validation.model.predict(try_X)
+
+# %%
+try_y.rename(columns={"NA": "NA_true"}, inplace=True)
+try_y["NA_predicted"] = Y_predicted
+NA_long = pd.wide_to_long(
+    try_y.reset_index(),
+    i="index",
+    j="value",
+    stubnames="NA",
+    sep="_",
+    suffix=".+",
+)
+
+# %%
+g1 = sns.displot(NA_long, x="NA", hue="value", binwidth=0.1, height=5, aspect=1.5)
+sns.move_legend(g1, "upper left", bbox_to_anchor=(.55, .45))
+g1.set_axis_labels("Daily mean", "Day count")
+
+display(g1)
+g1.savefig("prox_comm_PANAS_predictions.pdf")
+
+# %%
+from sklearn.metrics import mean_absolute_error
+
+# %%
+mean_absolute_error(try_y["NA_true"], try_y["NA_predicted"])
+
+# %%
+model_loso_mean_absolute_error

rapids

@@ -1 +1 @@
-Subproject commit f78aa3e7b3567423b44045766b230cd60d557cb0
+Subproject commit 8a6b52a97c95dcd8b70b980b4f46421b1a847905