Present results.

machine_learning/results_presentation.ipynb

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+# %%
+import datetime
+import importlib
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import yaml
+from pyprojroot import here
+from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate, cross_val_predict
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+from sklearn.dummy import DummyRegressor
+from sklearn.decomposition import PCA
+import xgboost as xg
+from IPython.core.interactiveshell import InteractiveShell
+InteractiveShell.ast_node_interactivity = "all"
+
+nb_dir = os.path.split(os.getcwd())[0]
+if nb_dir not in sys.path:
+    sys.path.append(nb_dir)
+
+import machine_learning.helper
+
+# %%
+csv_name = "./data/daily_18_hours_all_targets/input_JCQ_job_demand_mean.csv"
+
+# %%
+data_x, data_y, data_groups = machine_learning.helper.prepare_model_input(csv_name)
+
+# %%
+data_y.head()
+
+# %%
+scores = machine_learning.helper.run_all_models(csv_name)
+
+
+# %% jupyter={"source_hidden": true}
+logo = LeaveOneGroupOut()
+logo.get_n_splits(
+    data_x,
+    data_y,
+    groups=data_groups,
+)
+
+# %% [markdown]
+# ### Baseline: Dummy Regression (mean)
+dummy_regr = DummyRegressor(strategy="mean")
+
+# %% jupyter={"source_hidden": true}
+lin_reg_scores = cross_validate(
+    dummy_regr,
+    X=data_x,
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
+)
+print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
+print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
+print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
+print("R2", np.median(lin_reg_scores['test_r2']))
+
+# %%
+rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
+rfr_score = cross_validate(
+    rfr,
+    X=data_x,
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
+)
+print("Negative Mean Squared Error", np.median(rfr_score['test_neg_mean_squared_error']))
+print("Negative Mean Absolute Error", np.median(rfr_score['test_neg_mean_absolute_error']))
+print("Negative Root Mean Squared Error", np.median(rfr_score['test_neg_root_mean_squared_error']))
+print("R2", np.median(rfr_score['test_r2']))
+
+# %%
+y_predicted = cross_val_predict(rfr, data_x, data_y, groups=data_groups, cv=logo)
+
+# %%
+g1 = sns.relplot(data=data_y, x="y_true", y="y_predicted")
+#g1.set_axis_labels("true", "predicted")
+g1.set(title="Negative affect, Random Forest")
+display(g1)
+g1.savefig("d18NArfr_relplot.pdf")
+
+# %%
+data_y = pd.DataFrame(pd.concat([data_y, data_groups], axis=1))
+data_y.rename(columns={"target": "y_true"}, inplace=True)
+data_y["y_predicted"] = y_predicted
+
+# %%
+data_y.head()
+
+
+# %%
+data_y_long = pd.wide_to_long(
+    data_y.reset_index(),
+    i=["local_segment", "pid"],
+    j="value",
+    stubnames="y",
+    sep="_",
+    suffix=".+",
+)
+
+# %%
+data_y_long.head()
+# %%
+g2 = sns.displot(data_y_long, x="y", hue="value", binwidth=0.1, height=5, aspect=1.5)
+sns.move_legend(g2, "upper left", bbox_to_anchor=(.55, .45))
+g2.set(title="Negative affect, Random Forest")
+g2.savefig("d18NArfr_hist.pdf")
+
+# %%
+pca = PCA(n_components=2)
+pca.fit(data_x)
+print(pca.explained_variance_ratio_)
+
+# %%
+data_x_pca = pca.fit_transform(data_x)
+data_pca = pd.DataFrame(pd.concat([data_y.reset_index()["y_true"], pd.DataFrame(data_x_pca, columns = {"pca_0", "pca_1"})], axis=1))
+
+# %%
+data_pca
+# %%
+
+g3 = sns.relplot(data = data_pca, x = "pca_0", y = "pca_1", hue = "y_true", palette = sns.color_palette("Spectral", as_cmap=True))
+g3.savefig("d18NArfr_PCA.pdf")
+
+# %%