Add PCA for composite target.

exploration/ml_pipeline_classification_composite.py

@@ -17,6 +17,8 @@
 from pathlib import Path
 
 import pandas as pd
+import seaborn as sns
+from sklearn.decomposition import PCA
 
 from machine_learning.helper import (
     impute_encode_categorical_features,
@@ -80,9 +82,25 @@ for target in TARGETS:
         )
     print(all_features_cleaned.shape)
 
+# %%
+pca = PCA(n_components=1)
+TARGETS_PREFIXED = ["phone_esm_straw_" + target for target in TARGETS]
+pca.fit(all_features_cleaned[TARGETS_PREFIXED])
+print(pca.explained_variance_ratio_)
+
+# %%
+model_input = all_features_cleaned.drop(columns=TARGETS_PREFIXED)
+model_input["target"] = pca.fit_transform(all_features_cleaned[TARGETS_PREFIXED])
+
+# %%
+sns.histplot(data=model_input, x="target")
+
+# %%
+model_input.target.quantile(0.6)
+
 # %% jupyter={"outputs_hidden": false, "source_hidden": false}
 # bins = [-10, 0, 10] # bins for z-scored targets
-BINS = [-1, 0, 4]  # bins for stressfulness (0-4) target
+BINS = [-10, 0, 10]  # bins for stressfulness (0-4) target
 print("BINS: ", BINS)
 model_input["target"], edges = pd.cut(
     model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True