Testing auto-sklearn

automl_test.py

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+from pprint import pprint
+import sklearn.metrics
+import autosklearn.regression
+
+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 sklearn import linear_model, svm, kernel_ridge, gaussian_process
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+
+model_input = pd.read_csv("data/processed/models/population_model/z_input.csv") # Standardizirani podatki
+
+model_input.dropna(axis=1, how="all", inplace=True)
+model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
+
+categorical_feature_colnames = ["gender", "startlanguage"]
+categorical_features = model_input[categorical_feature_colnames].copy()
+mode_categorical_features = categorical_features.mode().iloc[0]
+categorical_features = categorical_features.fillna(mode_categorical_features)
+categorical_features = categorical_features.apply(lambda col: col.astype("category"))
+if not categorical_features.empty:
+    categorical_features = pd.get_dummies(categorical_features)
+numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
+model_in = pd.concat([numerical_features, categorical_features], axis=1)
+
+index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+model_in.set_index(index_columns, inplace=True)
+
+X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.20)
+
+automl = autosklearn.regression.AutoSklearnRegressor(
+    time_left_for_this_task=1200,
+    per_run_time_limit=60
+)
+automl.fit(X_train, y_train, dataset_name='straw')
+
+print(automl.leaderboard())
+pprint(automl.show_models(), indent=4)
+
+train_predictions = automl.predict(X_train)
+print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
+test_predictions = automl.predict(X_test)
+print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
+
+import sys
+sys.exit()