Feat - Prepared a new version release

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
+## [0.5.1] - 2023-10-19
+### Improvement
+- Improved documentation, thanks to @g1za
+- Bumped skforecast to 0.10.1
+- Added a new initial script for exploration of time series clustering. This will one day replace the need to configure the house load sensor with substracted deferrable load consumption
+### Fix
+- Updated automated tesing, dropped support for Python 3.8
+
 ## [0.5.0] - 2023-09-03
 ### Improvement
 - Finally added support for ingress thanks to the work from @siku2

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'David HERNANDEZ'
 
 # The full version, including alpha/beta/rc tags
-release = '0.5.0'
+release = '0.5.1'
 
 # -- General configuration ---------------------------------------------------
 

scripts/load_clustering_stumpy.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import pandas as pd
+import pathlib, pickle, copy, time
+import plotly.express as px
+import plotly.io as pio
+pio.renderers.default = 'browser'
+pd.options.plotting.backend = "plotly"
+
+from emhass.retrieve_hass import retrieve_hass
+from emhass.forecast import forecast
+from emhass.utils import get_root, get_yaml_parse, get_days_list, get_logger
+
+from sklearn.cluster import KMeans
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import ElasticNet
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import r2_score, silhouette_score
+
+from skforecast.ForecasterAutoreg import ForecasterAutoreg
+from skforecast.model_selection import bayesian_search_forecaster
+from skforecast.model_selection import backtesting_forecaster
+from skforecast.utils import save_forecaster
+from skforecast.utils import load_forecaster
+from skopt.space import Categorical, Real, Integer
+
+
+# the root folder
+root = str(get_root(__file__, num_parent=2))
+# create logger
+logger, ch = get_logger(__name__, root, save_to_file=True)
+
+
+if __name__ == '__main__':
+
+    days_to_retrieve = 240
+    model_type = "load_clustering"
+    var_model = "sensor.power_load_positive"
+
+    data_path = pathlib.Path(root+'/data/data_train_'+model_type+'.pkl')
+    params = None
+    template = 'presentation'
+
+    if data_path.is_file():
+        logger.info("Loading a previous data file")
+        with open(data_path, "rb") as fid:
+            data, var_model = pickle.load(fid)
+    else:
+        logger.info("Using EMHASS methods to retrieve the new forecast model train data")
+        retrieve_hass_conf, _, _ = get_yaml_parse(pathlib.Path(root+'/config_emhass.yaml'), use_secrets=True)
+        rh = retrieve_hass(retrieve_hass_conf['hass_url'], retrieve_hass_conf['long_lived_token'], 
+        retrieve_hass_conf['freq'], retrieve_hass_conf['time_zone'],
+        params, root, logger, get_data_from_file=False)
+
+        days_list = get_days_list(days_to_retrieve)
+        var_list = [var_model]
+        rh.get_data(days_list, var_list)
+
+        with open(data_path, 'wb') as fid:
+            pickle.dump((rh.df_final, var_model), fid, pickle.HIGHEST_PROTOCOL)
+
+        data = copy.deepcopy(rh.df_final)
+
+    logger.info(data.describe())
+
+    # Plot the input data
+    fig = data.plot()
+    fig.layout.template = template
+    fig.update_yaxes(title_text = "Power (W)")
+    fig.update_xaxes(title_text = "Time")
+    fig.show()
+
+    data_lag = pd.concat([data, data.shift()], axis=1)
+    data_lag.columns = ['power_load y(t)', 'power_load y(t+1)']
+    data_lag = data_lag.dropna()
+
+    fig2 = data_lag.plot.scatter(x='power_load y(t)', y='power_load y(t+1)', c='DarkBlue')
+    fig2.layout.template = template
+    fig2.show()
+
+    # Elbow method to check how many clusters
+    # distortions = []
+    # K = range(1,12)
+
+    # for cluster_size in K:
+    #     kmeans = KMeans(n_clusters=cluster_size, init='k-means++')
+    #     kmeans = kmeans.fit(data_lag)
+    #     distortions.append(kmeans.inertia_)
+
+    # df = pd.DataFrame({'Clusters': K, 'Distortions': distortions})
+    # fig = (px.line(df, x='Clusters', y='Distortions', template=template)).update_traces(mode='lines+markers')
+    # fig.show()
+
+    # The silouhette metod
+    silhouette_scores = []
+    K = range(2,12)
+
+    for cluster_size in K:
+        kmeans = KMeans(n_clusters=cluster_size, init='k-means++', random_state=200)
+        labels = kmeans.fit(data_lag).labels_
+        silhouette_score_tmp = silhouette_score(data_lag, labels, metric='euclidean', 
+                                                sample_size=1000, random_state=200)
+        silhouette_scores.append(silhouette_score_tmp)
+
+    df = pd.DataFrame({'Clusters': K, 'Silhouette Score': silhouette_scores})
+    fig = (px.line(df, x='Clusters', y='Silhouette Score', template=template)).update_traces(mode='lines+markers')
+    fig.show()
+
+    # The clustering
+    kmeans = KMeans(n_clusters=6, init='k-means++')
+    kmeans = kmeans.fit(data_lag)
+    data['cluster_group'] = kmeans.labels_
+
+    fig = px.scatter(data, x='power_load y(t)', y='power_load y(t+1)', color='cluster_group', template=template)
+    fig.show()

setup.py

@@ -19,7 +19,7 @@
 
 setup(
     name='emhass',  # Required
-    version='0.5.0',  # Required
+    version='0.5.1',  # Required
     description='An Energy Management System for Home Assistant',  # Optional
     long_description=long_description,  # Optional
     long_description_content_type='text/markdown',  # Optional (see note above)