{"cells":[{"metadata":{},"cell_type":"markdown","source":"![image](https://www.technotification.com/wp-content/uploads/2018/09/Renewable-Energy-Ideas-1200x600.jpg)\nImage source: [technotification](https://www.technotification.com/2018/09/amazing-renewable-energy-ideas.html)"},{"metadata":{},"cell_type":"markdown","source":"This notebook aims to predict a building's energy consumption over 2017 and 2018 using the data from 2016 in 4 different consumpiton categories (electricity, chilled water, steam, hot water) using ASHRAE data, which is our problem statement as well.\n\nThis is a supervised machine learning model, meaning based on the columns available in the datasets and data from 2016, we are going to train the model to predict an energy consumption of a building in each category. Since, consumption values are labeled as meter_reading and they are continuous, we are going to apply regression techniques to generate predictions on meter_reading.\n\nIt is a highly debated and popular competition in Kaggle currently, however my main motivation is to contribute to make energy-efficient buildings by estimating its energy consumption. It seemed like a good start to save our energy for future!\n\nThere will be 3 notebooks covering the complete machine learning building pipeline.\n\n[Notebook 1](https://www.kaggle.com/cereniyim/save-the-energy-for-the-future-1-detailed-eda) covered understanding the data and detailed EDA.\n\nThis notebook will focus on parts 3, 4 and 5; feature engineering, building the machine learning model and tuning its parameters.\n\n\n1) Understand, Cleand and Format Data\n\n2) Exploratory Data Analysis\n\n**3) Feature Engineering & Selection**\n\n**4) Compare Several Machine Learning Models**\n\n**5) Perform Hyperparameter Tuning and Cross Validation**\n\n6) Evaluate Model with Test Data\n\n7) Interpret Model Results\n\n8) Submissions & Summary & Conclusions\n\n[Notebook 3](niyim/save-the-energy-for-the-future-3-predictions) will generate the predictions with the selected model in this notebook and interpret them with an overall summary.\n\nMachine Learning application and building is not a linear and one time process. Steps above enable me to follow a structured way for an end-to-end machine project flow and preparation for the each step ahead. All in all, steps might be modified or revisited according to findings. You can use the table of contents to navigate to each section 👇\n\nEnjoy reading !"},{"metadata":{},"cell_type":"markdown","source":"# <a id='top'> Table of Contents </a>\n- <a href='#1'>Quick Recap from the Previous Notebook</a>\n\n\n- <a href='#3'> 3. Feature Engineering & Selection </a>\n  - <a href='#3-1'> 3.1. Feature Generation & Imputation </a>\n    - <a href='#3-1-1'> 3.1.1. Building features transformation </a>\n      - <a href='#3-1-1-1'> 3.1.1.1. Age from year built </a>\n      - <a href='#3-1-1-2'>  3.1.1.2. Primary use  </a>\n    - <a href='#3-1-2'> 3.1.2. Weather features transformation </a>\n      - <a href='#3-1-2-1'> 3.1.2.1. Add time related data from timestamp </a>\n      - <a href='#3-1-2-2'> 3.1.2.2. Impute missing values for weather variables </a>\n      - <a href='#3-1-2-3'>  3.1.2.3. Convert wind direction  </a>\n    - <a href='#3-1-3'> 3.1.3. Combine numeric features </a>\n    - <a href='#3-1-4'> 3.1.4. Merge dataframes </a>\n  - <a href='#3-2'> 3.2. Feature Selection & Further Imputation </a>\n    - <a href='#3-2-1'> 3.2.1. Pearson coefficients of the features </a>\n    - <a href='#3-2-2'> 3.2.2. Imputation of Train Dataset </a>\n  - <a href='#3-3'> 3.3. Split train set into training and validation set </a>\n  - <a href='#3-4'> 3.4. Create a Baseline Metric </a>\n\n\n- <a href='#4'> 4. Compare Several Machine Learning Models </a>\n  - <a href='#4-1'> 4.1. Linear Regression </a>\n  - <a href='#4-2'> 4.2. KNeighbors Regressor</a>\n  - <a href='#4-3'> 4.3. Light GBM </a>\n  - <a href='#4-4'> 4.4. Setting the initial parameters of light gbm </a>\n\n\n- <a href='#5-1'> 5. Perform Hyperparameter Tuning on the Best Model </a>\n- <a href='#5-2'> 5. Updated on Version10: Perform cross validation on the Light GBM </a>\n- <a href='#6'> Conclusions </a>"},{"metadata":{},"cell_type":"markdown","source":"**Imports:**\nI will mainly use scikit-learn library for the machine learning model building and give the light gbm library a try. For the hyperparameter tuning I will use hyperopt."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# for data manipulation\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# pandas options\npd.set_option('display.max_columns', 50)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\npd.set_option('mode.use_inf_as_na', True)\npd.options.mode.chained_assignment = None\n\n# for date manipulation\nfrom datetime import datetime\n\n# for visualization: matplotlib\nfrom matplotlib import pyplot as plt\nfrom IPython.core.pylabtools import figsize\n%matplotlib inline\n# to display visuals in the notebook\n\n# for visualization: seaborn\nimport seaborn as sns\nsns.set_context(font_scale=2)\n\n# for feature engineering: itertools\nfrom itertools import combinations\n\n# for data preprocessing\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import MinMaxScaler\n\n# for building the model and calculate RMSE\nimport lightgbm as lgb\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\n\n# for hyperparamter optimization and the cross-validation search\nfrom hyperopt import hp, tpe\nfrom hyperopt.fmin import fmin\nfrom sklearn.model_selection import KFold\n# from sklearn.model_selection import cross_val_score\n# from sklearn.model_selection import TimeSeriesSplit\n\n# for model explainability\nimport shap\n\n# to cleanup memory usage\nimport gc\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# load train  data\nbuilding = pd.read_csv(\"/kaggle/input/ashrae-energy-prediction/building_metadata.csv\")\nweather_train = pd.read_csv(\"/kaggle/input/ashrae-energy-prediction/weather_train.csv\")\ntrain = pd.read_csv(\"/kaggle/input/ashrae-energy-prediction/train.csv\")\n\n# drop floor_count\nbuilding.drop(columns=[\"floor_count\"], inplace=True)\n\n# convert timestamp column of weather_train and train\ntrain[\"timestamp\"] = pd.to_datetime(train[\"timestamp\"],\n                                   format='%Y-%m-%d %H:%M:%S')\nweather_train[\"timestamp\"] = pd.to_datetime(weather_train[\"timestamp\"],\n                                            format='%Y-%m-%d %H:%M:%S')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"## Function to reduce the DF size\n# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin\ndef reduce_memory_usage(df, verbose=True):\n    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n    start_mem = df.memory_usage().sum() / 1024**2    \n    for col in df.columns:\n        col_type = df[col].dtypes\n        if col_type in numerics:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float16)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)    \n    end_mem = df.memory_usage().sum() / 1024**2\n    if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"reduce_memory_usage(building)\nreduce_memory_usage(weather_train)\nreduce_memory_usage(train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# <a id='1'> Quick Recap from the Previous Notebook </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"In the previous notebook we had a look at the  distirbution of the target(meter_reading) from various aspects and its relationship of each variable to the meter reading values. Moreover, we observed distribution, missing values and time-series plot of each individual feature.\n\n**Here is the distribution of the natural logarithm of the meter reading+1 among different meter categories:**\n\n![image.png](attachment:image.png)\n\nThe training data from 2016 has 20 million rows, and using that data we are going to predict 40 million rows of 2017 and 2018 data in different meter categories. \n\nThere are 15 columns to use as a potential feature set. Some features highly impacting the energy consumption, like square_feet and air_temperature. However, some are not that much significant. \n\nIf a not-signifciant column has also high missing values like floor_count, it will be dropped. Others will be transformed into categorical features (like wind_direction). Other weather data will be used for feature generation.","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"# <a id='3'> 3. Feature Engineering & Selection </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"In this part, using the findings from the EDA and with some human interpretation, I will generate more features. \n\nAdding and extracting more features based on the existing ones and deciding on which ones to use whether with an algorithm or by manual selection using the human or domain knowledge is called feature enegineering. (definition from wikipedia is [here](https://en.wikipedia.org/wiki/Feature_engineering)).\n\nSome common options for feature engineering:\n* automatic tools like [featuretools](https://www.featuretools.com/): this does all the process defined for us, and I gave it a shot to use in this project. I guess due to the amount of data we have, I faced some runtime errors.\n* transformations of numerical columns: by taking the logarithm and square root\n* transformation of categorical columns: with several encoding techniques\n* combination of numeric features: by creating linear combinations of the numeric features\n* manual approach: looking at the definitions of the features and by understanding the relationship between them\n<br> I will follow the manual and combination approach 👆</br>\n\nWe have 15 unique columns in all three train datasets. I think 15 is a relatively low number for a feature set if we think that we have 40 million rows to predict in the test set. Using very few features might end up in underfitting.\n\nMoreover, when I search my own ML notes, here is the one of the valuable learnings I got from Andrew Ng's Machine Learning course is (quoting from my personal notes):\n\n> To get a low test error, use algorith with many features. This will ensure to create a low-bias algortihm. On the other hand, if you feed your algorith with a very large training set you will lower the variance. This way you can balance out the bias and variance, increasing the probablity of getting a low test error model.\n\nSo, for this specific project, I am going to impute each dataset first then add and transform features:\n\n* tranformation numerical features into categorical\n  * wind_direction\n  * primary_use\n* extract features from existing columns\n  * from year_built\n    * age of the building\n  * from timestamp\n    * month\n    * hour\n    * day of the month\n    * day of the week\n    * is_weekend\n    * season\n* combine numeric features\n  \nMoreover, I will try a different approach and impute the missing values at the same time with the feature generation before merging the 3 train data frames.\n\nMoreover, I will change imputing strategy to mean, meaning:\n- for the building dataset I will impute the columns with the mean\n- for the weather_train dataset I will imputer each weather variable with the daily mean for that month of the measurements in each site\n\nAfter the generation and imputing, I will look at the pearson coefficients of the features to the target and look at the [collinearity](https://en.wikipedia.org/wiki/Multicollinearity) (to be discussed later on!) between the features to decide on the final set."},{"metadata":{},"cell_type":"markdown","source":"## <a id='3-1'>  3.1. Feature Generation & Imputation </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-1-1'> 3.1.1. Building features transformation </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"When we are loading the datasets, I dropped floor_count due to the 75% of the data is missing. For the building dataset, I am going to convert year_built to age column as I did for the visualization case and impute missing values of the building_age with the mean.\n\nAnd I will tranform the primary_use column to a numeric column by using one of the encoding strategies."},{"metadata":{},"cell_type":"markdown","source":"#### <a id='3-1-1-1'> 3.1.1.1. Age from year built </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"We observed that when we transform year_built values it will scale it to 0 to 120 which bring better intepretability to our model. \n\nAfter adding age I am going to drop year_built because they are providing the same information to the model. As a final step, I will fill missing values with the mean."},{"metadata":{"trusted":true},"cell_type":"code","source":"# add building age column\ncurrent_year = datetime.now().year\nbuilding['building_age'] = current_year - building['year_built']\nbuilding.drop(columns=['year_built'], inplace=True)\n\n# since NA values only present in building age fillna can be used\nbuilding.fillna(round(building.building_age.mean(),0),\n                inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# check if any NA values left\nbuilding.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### <a id='3-1-1-2'>  3.1.1.2. Primary use  </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"There are several options for encoding a categorical variable: label encoding, one-hot-encodig, count-encoding and so on. \n\nI want to have only one column for primary_use, so I am going to keep it simple and go with the label encoding."},{"metadata":{"trusted":true},"cell_type":"code","source":"# create label encoder object and transform the column\nle = LabelEncoder()\nle_primary_use = le.fit_transform(building.primary_use)\n\n# add label encoded column to dataframe\nbuilding['primary_use'] = le_primary_use\n\ndel le, le_primary_use\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-1-2'> 3.1.2. Weather features transformation </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"#### <a id='3-1-2-1'> 3.1.2.1. Add time related data from timestamp </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Recall that we found some seasonality factors in the time-series data especially for the chilled and hot water consumption.\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABjoAAAMCCAYAAADQzx82AAAgAElEQVR4AezdBZgV1RvH8Zfuku5uUECQUEARFbBBVOzA7o6/iomt2N2JnaCAoKAiEoJ0lwhId8P/+c0yszN3b+2yNfA9z7N7p+fMZ+beu3veE3n27Nmzx0gIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAgF8oYwz2QZAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHAECHTwICCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAg0MEzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEVINAR2ltHxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBAB88AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhFaAQEdobx0ZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQIdPAMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWgECHaG9dWQcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECHTwDCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBoBQh0hPbWkXEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIDwECCCCQmwUGvDLQfhk90cniOacdZ71OODI3Z5e8IZCUwIaNm23t+o1WolgRK12qRFL7sBECCBy4Atu2bbczL+vnAbz7/N1WonhRbz6sE98P/d1e/+BbJ/ttWzax2687N6yXQr4RQAABBBBAAAEEEEAghwUIdOTgDRg2cqx9/u3PgRzccf15VqNqxcAyZhCIJzDitwn2yVc/pdkkT548VrBAfitcuJAVLlzQypctbbVrVLFG9WpYnVpV02yf3Qt+/m2CPfzMu95pX3nyNqtVvbI3704sX7Ha5i1Y4syuXbfBXZyrXpcsXWEPPvX2PuXp4buvsNIli+/TMdg59wosWrLcVKD3x7gpNnXmfNuxY6eXWb0/q1QsZ1Url7cjD29l3bu0s2LFinjrmUAgKwWefe1T+37ob84pSpYoZp++8VBWni5XHXvN2g125qV3e3m68qJednK3jt58bprYvWeP912ofO3avTvD2Xt34GAbPW6Ks3+j+jXtuktPT3islavX2d0Pv+ptd97p3a19m2befKyJISPG2JeDRjqrG9arYddfdkZg0/UbNnnXxd+/ARpmEEAAAQQQQAABBBBAIJ0CBDrSCZaZm7/3yQ82ccrswCG//fFXu+qiXoFlzCAQT2DZ8lX2+9jJ8TZJs65Jg1rW68SjrPvR7a1Y0cJp1mfHgq3bttuy/1Z7p9q5c5c3HbaJzVu2pvseRF7j9u07Ihcxvx8I7Nq1y94ZONhefvtL2xbjHm/dut3mLfzX+Rn1xyR7/IUP7JjOh9kpPTrZoQc3NAUtSQgkI3Dxdf3t3+UrnU0fuftKO6RpvYS7rduw0fss1rN4ICUFC/zfQ/osPxCSAqnu3w2Tps2xa/qeZnnzxu/NdtzE6d4+MqpSuVxSgY7BP/3h7dekYe0DgZdrRAABBBBAAAEEEEAAgRwSiP9fTQ5l6kA47T///pcmyKHrHjRstO3Zs+dAIOAac1Bg2qwF9sCTb1nXXtfa+5/9mIM54dQI7L8CM2YvtLMuv9eeefWTQJAjX758VrdWVetyxKGm2tRqeeVPKmxW0FuF1vMXLfWvYhqBuAJqAffvspXOj7o6IiEQTaBtqybe4k2bttj0WQu8+VgTYydOD6wa99eMwHy0Gf09O/7v1O0Oa9U42mYsQwABBBBAAAEEEEAAAQQyRSBYupIph+QgyQioCxM3HXpIQ/tr8izbvXuPuQGQls0buKt5RSBpgSqVypn67XbTlq3bbPXaDbZm3QZbvWa9jZkw1X7+dYJX6Kraq48//4HNnrvY/nfjBWkKXN3jZMWraho/cvcV3qErVTjImw77xAuP3mwHlSmZrss4qHT6tk/Xwdk42wUmT59r51/9oKlFh5s6tGlu117S2+rVrmYFfMENbbNg8TKbOWeRfTVopPM+dffhFYGsFujZo7O1PqSRcxr/c5nV580Nx9cYF/7voSYNDowWB9WqVHC6ylO3i0p//jXdmjaqE/eWRAY2FixeaurOqtxBpWLuN3veP7Zu/SZnfaGCBaxFM/62jYnFCgQQQAABBBBAAAEEENhnAQId+0yYsQN8NySlP2ztrX6OCxYo4PWXrHUEOjLmeqDvlT9fPitfrkyAoUa1St68BvLeuGmz/TB8jL3yzlf238o1zrqvBo80dSX16D1Xettm9UTliuVMP/tjalC3ulWIuA/743VyTdEFdu/ebQ88+bYX5FAB3w2Xn2lnnto1ajdUbgsPtfLo0bW9Tfh7pt3/xJu05ojOy9JMFlABd6JC7kw+Za45nN6b6sLxQEztDm1qn3+XMk7c2L+m24V9jo/JoICGAhtKGsdF42ooqTurbl3aOdPRfmm9mw5uWs/kTUIAAQQQQAABBBBAAAEEskqArquySjbOcf+eNsc0MK2SBh8+ou0h1qNrB2+PIT//adt9A9V6K3LBRJjHUYjH5691HW+79K7LquOmNx/+7YsXK2qnnXiUffL6g3aYr/uKH4b/Yd/88Kt/U6ZDIJDZz1hueY/nlnxk5BH46IuhNnPOQm/XJ+67xvr0PCZqkMPbyDfR6uCG9vGrD1jP44+0/PmS/5rO7GfBlyUvaONftj9Pq4VlerqRVHArPdsnY6fjKR+5PaXkM+MDY8e7vqxwjXe+fVmXle+/fclXrH3btmrqrVKr4nj5Hz8xtfupc3t38/Yb51vuLfRNjJuUup//fL5NkpoM03OQ1AWxEQIIIIAAAggggAACCGSJAC06soQ1/kH9rTmOPaqt5c+fz47u1NoefPptU5/aqik3cvRf1rVTm5gHmjpzvv04fIyzPl++vHb1xb1MtYKTSQO/+snc7grat2lm7Vs3i7nb6HFT7Lc//7ZZcxbbrHmLbN36jVatcgWrU6uqNW1U2/QPb5HChWLur1qA7w4c7KxXTb6rLk4daF3/VM+au9jUxcvS5atMXUh07dTa3BYI6u7g97F/m8aTmLdgia1as97pfmntug2mgTTVXULZg0rZoQc3st4nHWWlS5WImY9oK/6cMM1+GjXOJk+fZ7PmLnIG4lT3SepWRoEADZr55gffObuWLlXcLjrrhGiHCSzTvfvi+1+c46kbGvWvL5+6tapY3VrV7IRjDzd1VZYbUpnSJez5R26ysy7rZ3Pm/+Nk6ZFn37WO7Q4xrYtMKngePW6yTZkx32bPW2wrVq61VWvWOfdEz3DK/Sht9etUszNO6Wq1a1SOPERgXvfxzQ+/d5bpGb7u0tMD65OZ0bPxw09/eMdIz/vgoy+H2tJlq5x9OxzW3FS7NbelF9/6wtzBgc/tfZzTWkfdjX09eJRNmjrHee/ovVy5Ylnvue3coWW6LiMn3+P+jKqf+IFf/2STp821v6fPtVWr15m686pcqax1atfCTjvpKFPXKfrsU+p+dDtr3KCWd4jc8iysWLnGXnjzcy9f+ozt1L6FN5/sROHCBa3fLRfF3XzHjp3O583UGfNsxpxFNm/hEufzpmG9GtaoXk3Ts9CmZeI+8bPyOVPQ/ovvfnbGpFr0zzInyK/vqioVy1rlSuWsYd0adlyXtlarevTPi/c+/cH5rBHEyd07OmObxEOZOGW2DR813tmkbu2qdnK3jmk2j/W9pO+ZQT+Ndt5bMi1QoIDTpVPrFo2se9f2TsUE/8FGj53itMTUd5jGONCg8WqV06JZfbviwp5WrGhh/+aB6Vh50HGUfx1zyox5tmvXbmvSoJY1b1LXzjjl6Jit4DQOjLZdu36jd55Pvx1hv47525t3J+rUqmKndO/kzjrnGvrzWGe+ZvVKppZ/idKI3ybYH+OmON2t6ftT3w/16lS3RvVqOC1S9V0nj3gp1r1yl0+ZMdemzUwZO0L38uAm9ezKC3s6fyvEO25617363te2ceMWZ7c+PbtGNdaYOeqGSenIw1uagpEK8AwZ8af9+dc05+8IfY+WLFHU+SzWZ9Y5vbtZ3rzxDdKb1/Rsv3LVWtP7xx32rXrVCtb7pC7eIVTRQfdI16HvFX22yjhacsfn0Hf9Wb2Osbc++t7Zx99iI3I/HdcfIPFXrIjcNtp8dj8H0fLAMgQQQAABBBBAAAEEEAiXAIGObL5fKphSzXk3HX9MSksOFYjon2c3ePHtj7/FDXRUrlDWPvj8R6dwQcfSeAdHHt7KPWzMV43T8Njz73v7HX9saksS/076p/ex5z+wL7//xb/YmVZrFP38/NsEp8D1vtv6ev17R268Zu16e2fgIGexghMKdKhA840Pv7MPPx9iGkPCnxRQcAMdI34bb/0efd2/2pvetn2HU8BucxebCptee/8bO6vnMXb9ZWd428SaUDDpyZc+MgV8ItPCxctMPz+NHOcU9q9Zu8HZpHqVCgkDHX+Mn2p3P/yq1x2Ue2x1FaVCaf18OegXO/OUrnbdZafHDRC5+2b1q4JP6p/89L53m2pMbtq81QYN+93OPu24NKfWILdX3/5UmuXugg0bNzuBHRV8fPzlMCdg8sjdV8Ys7FNQyH021C98RgIdKiz96IshXgsoFXwfe+RhbpZivi77b7U99twHzjVro1O6py0MjblzNq74+MuhXv/m6tJIhfn9B7xjyr8/uYMPjxw90QnS3Xn9eQkDn7nhPe5ew/hJM+1//V+xpctXuoucVwXS9DNl+jznPV6wYAHn80MrVZjvD3TklmfhvU9/dN5HymPevHntlqvODlxTZs0oaHDb/S86z4T/mPqOUTc0+nn/sx/tnN7H2XWXnB4YE8S/vaaz6jnTWFTPvvZJmudV51SgU8+zPmsVaNG9fGPAnWk+L/Rd6LaOaXlwg4SBDgUK3M8VfSdGC3REfi9ddv4p9vr739jrH3xr8ktNW23YyLHOjwq6Xx9whxUtUthk/8BTb5uC5ZFJAQr9/DJ6og148LqY+Y3MwwVn9rDnXv/MBn49LE0rDtWK149a3D3T/3pr3rhu5Gnt/U9/8D4H3ZVDRqRUhnDn3VcF3vyBDlU4cM0UmIsX6NB3xENPv23+ccbc4yo4pB91h6T1D955WdzxGyLv1ZEdWtlTL31kX/8wKk3rGH0G6Gf4qHH26D1XOcEk97z7+vrJ18NNAUolVTqJ1qWiAjt6VpXKlyttpUoWd7qXU2G8P+lvBvf9N/zX8fbEvVdbubKl/Ztky7T+1rvkxkds3sJ/nfPpb5g3nrkzcG5VaGhQt4b3/lK+YwU6xu9tmdGsUR1Tq1B1r6pKMKrMoc/osmXSjtOhwI8beNPfuM0aJTf+iSq55MRzEMBhBgEEEEAAAQQQQAABBEIpkHyfGKG8vNyX6VFjJnkFl1Urlw/8s368r/uqX8dM8v5BjHYVGui4yxGHequ+HDTSm443odYkbpcw+odWtWkjk2q19r74Li/IUbBAfuef31N6dLI+px7jtABR6wslDZ7e9/qHvfFFIo8VOa9CgV4X/c/e+ODbNEGOyG0j51XzUAUG6ku8fp3qgcGeFbxQDcP3PvkhcrfAvP6BPuOSu9MEOTSuhQqP1KLDbRnjBjkCB4gxM+CVgXb5zY95QQ7VsFdtarV4Uf/fGrNBSTUc1ZLgylufiHGk7F8sy7a+Lqy+9Y0fkyg3pUoWs4b1ajqFlCoccmvw6jpV6H7nQy+nKbBKdMz0rFcrnuN8/YNHC15FO95n3w73ghzqTkMtlHJ7euCpt+zaO58OFBoXKlQwTbY/+3aEJfo8yE3vcb0f+t7QPxDk0GdOo/o1nZrRKlRTUgG0gqSxUm55FlS7201HtD04ZkG3u01GXvXeOuOSewJBjorlD3JajqgAUoXxSnof6jPx3Kvuj/t94s9DZj1napWg978/KKfvLbUYUwuFyL76Veitz/HsTju273C+E156+0svyKFa+JGDcqu2+839nrfBP422My+9JxDkiLa9giH3Pf5GUpej6z75/Nud7wZ/V1WRRipQvvi6/s4YLkkdOJM30vf9GX3vCgQ59LeAWg0dftjBgcJutRTrffH/nOBEMtlQK8GTz7vVNF6UnlslBQoj74OepzsefMm7V8kcO7O3UfDt9L53Oa2U3GPrM8v9/nOXqTuoJ178yJ3NtlcFFy696VEvyFGjakUnyKHPiMjU9tAm3iK31Ya3YO+Enjs3YNK6RUoLMX9LMX+rDf++/m6rWh3cyPvbyr9N5HSYnoPIvDOPAAIIIIAAAggggAACOS9Ai45svgf+bqv8gQ1lQwUFGrND/6QqGKEueTR4baykrlw0nofSqD8mmbopSFRzUC0K3KT9I5MKGPo99oYTwNA6/TN7z00Xeq0s3O1VG7f/M+86LVC0z+PPf2CfvvFg3H9kt2zZahdd19/rB1o1/BRcUCGzCnpUK90t1NR5VGigQvTOHVrYkR1aOjUPIws91D2UuuxQzUKlp17+2A5ve7DVqVnFzWrg9ZlXB3oD/Or455/R3S448/hAV02q6T7i1wlOdyv+f9QDB/LN6NwKsiipgPHaS3o7XTdFdlmh2qAqRFQARYMNq2VPvEE8fafI8knlQwVTSipwVOuNaIUiCgQpgNOpvbruSC1QdTOoQii11HFrB6vVj7ryildD2N03o69qIaPa1kpqTaICmVj3X9uowNwdgFXzfXod4+ybmb90jmDN8MRHj3y2I/dQbWYlFWSf1O0Ia9m8odM9mD4vVKB276Ove4XZL7/zlZ147OEWLRCSm97ji//9z55+6WOvBrsK5O69ta+1aFYv8Fmi97kKmD/9ZoSphVSslNPPgvLmtj5QHuM9h7GuIdFytYK774k3nW5jtK26fHrivqud4K+7rwrLFcxTqzw9h3pPv/z2l3b7tee6m8R8zYznTAWjdz38incOBcjP7n2cqVa5m9SCbOE/y53PQXWvEy+I5e6TFa/qWks1z1Wo3vOEzta5fUvnPabvpwWLl9mbH37nfb7os979rlEXPvpcO6LtIc5noboo1PZ677ktKdSKTy39EnWLp+97tShQYfnpJx9t7Vo3c7qqKlm8mDMA9OBho50WTXrvqjXjC29+YW8MuCPAMfTzZxXZsj6X9XO+S7Xy6Qeuc/IW2NDMCuTP2J9+j7/woek9q6QWmvff1jdNy1O1zrr1/hecv0fUquD+J9+yj1+9P2EXTm53mnLU3z0KQGvw6sKFCtiiJf85lSPcz3n9rfDJ1z9FbXkYea1ZMe++xxXcV1dX6saqcf1azt9tepbUDanec0r6nldrHQVusyOpZaUqXShgoKQWsnpWKpQrE/X0ejbd7kUnTp7tXIOebX/yBzJaH9LIWdVmb8BDM2MnzjB1wxqZ1NWgm/wBFXdZtNcwPQfR8s8yBBBAAAEEEEAAAQQQyFmBjP23m7N5Du3ZU8bemOjlP7LbKP1zqX8W9Q+8koIi8QIdh7VsYioYVDdSGu9C3T1cfPaJ3vEjJ1To4tbKK1mimHWL8o/pNz/+ahosXUk191598vaoBRSqPf3IXVfagkXLnIK9uQuWOIXHKqSJlVJqqu5yCuRuv+5ca9W8gVO4FGv7k447Imq3I/7t1X3NgIeut3Muv9dmzl3k1NJX/qMVMOr6Vejupifvu8bppsKdd18VrFCXYuXLlrZxNz7iLo76qkLER59731mnwMmAh65zCmiibawuMbTNDXc/46x++uWPrUvH1k7hVrTts3OZxiPxJ42ZEhnoUAukHz8Z4N8szbQCITdecaZt2rLFPvtmhLNehfBZGeho1riONW1Y2xu74ZOvfjI9X7HS0F/GpnR7Zma6JhVsZnbq0eemdB9y6GfPxCyM0sHUNYhs1fe9Pyk4etThraz8ozfb2Vfc66xSoam6z3Fr3/q3z03v8YcHvOsU3Cp/GtfgxcduSdN1kdbpfa4fvYcVJIyVcvpZ0PvGXyNf4x1kdnr740FOIbKOq/Eg3nvhHqfQ2X8eBVn1WawgiLqvUVL3PPo+iTUWhrt/ZjxnKuxWF0dKar0R7f2owILG8bniglPt7F7H2jsDB6epve/mKatf1Uqw380XOc+Y/1z6Hnng9kts8ZLlgdr7ag2p8VM0npM/6X48evcVzvZuQfeIX8cnDHToGOpm6+arzgoEg7RceVCXj1WrlPe6clRAVwEpPe9u0ueAUr68qQ11ixcrku6xq9zjRb7qnApcKyko+/6L/aJ+z2oMqoGvPeC05lCgQ0GBb34YZWoRmiip20EZRH736Dl58I5Lne7OVKlDSS2GonWxmOgcmbFeAanzz+xhl5xzUiCYrL/hdE/eevZ/1v3MG51KDQpOqdur7Ah06D13xS2Pe0EWff688bSJsZUAACAASURBVPQdzthOsa5bQRrdT/0toyCqxoXRZ7E/uRU+1Nq1RfOUdU0a1nI+dxSg1LMRmXTd7n5al57xOcLyHEReM/MIIIAAAggggAACCCCQ8wKp/xHnfF72+xxo0Eq3lrcKZqMVOLljdghDBZUaLyJWUqF5rxNTW2V8NSi1y4do+2i9m1RYGlnbW//kPvPKJ84m6jJDBQuRrRLc/fWqdVdeeKq3SGNlxEva/pq+pzm1O1UrUAVd8ZKuL5mkQgcNJu0m9TceLak1h/75VurYrkXUIEe0/eIt+/CLId49Ov3kLjGDHO4xunQ81CvwUOuH+YtS+s921+fUqwYS96f/9vZX7l+WnumjDk/tVm3WnEXp2TVD257ha/n0zZBf43aLNvCrYd45NLhvvGfc2zAXTDzb/4Y0QQ5/tlTA5q8x/8+/K/yrnenc9B5Xf/Bu7Xhl7tarz44a5EhzEQkW5OSzsHZd6kDQyqY73lCCLCe9Wq3B3v44pfWYdup7zklpghz+g6lwsUOblM9GBcOfe+0z/+qo05nxnLmF/DqBguKJkgLv+m5wu0RMtH1mrldgW4X2CqRFS/oe8g8mr8oF7zx/d5ogh7uvvtfUPZeb1PogUVLriGceuj7w/o3cR2NqqMDfTR/7PsfcZVn5+tTLA73DqxJCtMoE7gb6PlG3jW567o3PvL993GWRr+rm7fF7r04T5PBvp24g3fTP0pSWJe58dr5eeVEvu/ri09L8DeXmQa1S/GOmqcuvrE76bL/qtiecvxt1rmSCHNpOeT3ENwC5Ppcjk1psKOnvVm2vpOe8VfOGzrQq0Cio5U9appa/Ss5YIHVSuu/0bxNtOkzPQbT8swwBBBBAAAEEEEAAAQRyViB+SXPO5m2/O/u3Q1K619GF9dg7CHnkRWpQcdUyd9N3Q39zJ6O+aqBVt8sbtexwB4yM3Fj/BP84InUQ9N6+AIm7rbqHUZcjSuoeR+NMJEqN6tfyNln4T+ygjDYqUqSwUzAX2S2Cd4B0TqjrExX86R9qd1wNHWLBoqVpjqQAx7SZKV1JaOXl55+SZpuMLPj9z8nebj18Y6x4C6NM+Gt2LvpneZQtsn9RZGGk+xykJycyVrcZeg79fe0vWJz2fqTnuMlsq6633BrNqmH6/ZDfo+6mZ9wdPLZw4YJ2ao/OUbfb14WqFa9xX9Lzkyjwl8z7xj84d7SCwNz0HteYB25S4ZZq1WdGyslnQc+/P1WtlPpZ7l+e0enpsxfY1q0p41ioK5puXdJ2FxN57PPO6O4tmjA5dmsYd6PMeM78AYs/xk8JjL/inie3vObJmydhsFPjGLlJXUclCo5qgGc3JRPocLdN9Nrz+CO9TdzWmd6CLJzQNU/zvV/PPT31mYp12jNOPtobh0XdarpdXsXaPn8S3Wk19nX/pGC8W3Ek1jGzanlS7xHf30ZZHejQ9+01dzzldGGoa1YlmjcH3Bm3JYffpu2hTb3ZyECHAhga00mpdYuUbqvcjQ9rmTJeh+b9rTc07z+OurlKtuJKmJ4D14FXBBBAAAEEEEAAAQQQyD0CdF2VTfdC/+S7Bawq0Ox+dLuoZ9Y/gz26trfX3vvGWf/90N/tygt7xvwnUTXlju7Y2ukHWjuoa6Zo3dX8OGKM15WIupaINviyP0BQtGhhr5/xqBndu3D3nj1OoY+6a9E/8yrsTvYf2njHjVynghINlKl/ptWP9IpVa2z9hs1eCw3/9qq5HJmW/bfKq+WvQqpYtXcj90s07xbi65jqW/q/FasT7WKb93bpog3VJUpuSBo/xZ/UN3y8pC4y1CWVAmvjJs5wrl1jRUSz37lrd7xDZco6tUA69fjO3lgp6v4t2hg0H3+Z2ppDY+SoJnlWpI9fuz9uN1RZcU4d01/AvHFj2oG7c9N73C08U779AZp9tcnJZ0HBM39avWZdUgFj/z7xpv2BUXWTlCg4pmP5C+lVaKlAoFoQ7EtK9JypOxw36bvhwmv720VnHZ/SxVqMsQLc7XPja9EiKbXYlbcdO3cmzKJ/+02b074PEx4gxgZ1a1f11ixbvsqbzuoJ97td51HFilpJdMmmZ0xjWLjfkRqcPV4rkGSuwf/c6bnatGWrlS6Q0mVXMvtn5zb+vG7clHnPQOQ1aIyZ6+4a4AUW1Orn9afvSDhem/84CnS88ObnzqKJU2c7ASS3Ao0/gBEZ6PAPSK7uq9TllJv83VlpvJXMTH7b3P4cZOZ1cywEEEAAAQQQQAABBBBILECgI7FRpmwxaGhqDXMN/qga37GSWga4gQ4VnitAohYWsVLvk47yAh3Dfhlrd1x3XqDAU/t96Rub4rQTu0Q9lFsgoZXq/1o/6Umq9amuWxR8yayk1gG3P/CSTZ2RMhBzRo87d0FqF1Hly5bxWsFk9HjaTzWrl69Y4xxC/2zf+dDL6T6cAjC5IUW24KhUIXprHg3Ufv8Tb5kCZ2pRk5tS75O6OIOg615ovBa9b/x9jau2/aCfUt+HfXpm/iDkOe2RqAA7N73H5y5MqSUss8xu+ZBTz0KpveMkuM+Bat1nVksVHVOfh26KHMfAXR75WrZMSVMNdA14raRj7GtgKdFzpvddlyMOteG/pnyHLF2+0h56+h3nRy0FmzasY00b1XYGcVYrxqwIjkc67Mt84UKpgY5kjpPe7ZM5prYpV7a0t+nqtetNhdzqujGrk7+1ploSJXu/NGaT+5mzeMm+d9+U6LnLaof0HF/jo2R10nv6pnueNbfbUAWSXlOQI6IrykT5aNaotjfehlqHqNtUN1jpthJWULVFs+DfoQ3r1XT+1tR3qyo8+JM/QHJYq9SWH/5tMjodpucgo9fIfggggAACCCCAAAIIIJAxgaz/Dzlj+drv9tLA4m76Z+kKpy9ldz7aq79g6tsff40b6FALDvXHrPE8FGxQKxD/IOYqaHBbk6iLoq6d20Q7pTfWRNSVSS7clYmF3xp09I4HX3K6Q3JPr5p8KqRT914qcClTqoSpcFHdarz36Q/uZmle/V1H+AuL0myYjgULlyyL2qIkHYewzPRKz3kjt/UXZGmdCqgik2rkXn/XM6aB592kQjYVdtSoVtHpW/2g0iWd+6GWHfc+/oa7Wba86pk4ou0hNnL0ROd8GovDH+j4+odRXrc/GiPGX9M9WzKYDSfJY/HHtYk35k+y2cusZ9Y/hkhmvSfda8ipZ+GgiCDv/Cjd6Ll5zMir/3NMn3/JJBVKly9b2jRQupK+f/Y10JHoOdN5Hut3lT3y3Hv2+bc/Bz4nlQ/9DBs51smPav1f2Od403g5uTUlOVyUl/30bu/tmGBC33duUutJjYGQ7HPg7peR1yW+8X7Scz513eemaF3pueuSfU02wJLs8bJyu6x6Bvx5vvPBl23MhKneosKFCnpjaHgLk5hQ15/6Tvzl97+crdXtlBvocLug0mdGsaKFA0dTS9ZDD2nkDFKvoK66ElVFF7XW07SS/pbI7LGKwvQcBMCYQQABBBBAAAEEEEAAgSwXINCR5cRmk6bOCdTEVYGxfpJNQ37+026/7ry4NTdPO+Eoe/Klj5xDfjnol0Cgwz8Iucb0iFUDVN1VuUkFT5ece7I7m/Srv8uOpHeKsuGEv2fatXc+Zaqdr6TClasu6mUndesYtX90t7ZylEM5i/wFkOvWBwcMjrVPouXFiqR6qbbjiK+etwJJ9DPuP67bPYR/WU5MDx81wTutgmb+cWK0Qt3dnHPlfbZu/SZnOw1kr8FmL+pzfNRucFSDOyeSAnxuoGPoL2PtlqvOtoPKlHQKWgd+9ZOXpT699r/WHN7FxZnITe9xvSfdwWoz6z3pv/SceBZUaK/Wem4LqfmLUluS+fOW0Wl/V2sb09Elkrqac5O/2xd3WVa86rPt7hsvtL5nn2hfDx5lv47522bMWZhmXAV9VvQf8I5Nmjrb+v/v8qzIyn5zTLfwWBek7/H01tzPKESJEkW9XdPTFZd/2+x67ryMHgATbpBDf3+oheW0WQvsuv89bS88erM3PkqyDGpp7AY6/vxrul12/ineGGg6RpuI8Tnc46r7qp9/m+B8x6oVxzGd25g7eLm2OaxVE3dTXhFAAAEEEEAAAQQQQACBLBdgMPIsJzbzt+bIyOnULYD7D2is/U/qnhrAmDF7oU2flTLwtmrWq0WIkmrBnXZi6mCmkcfy95+tGv6qvZfen8yqaffeJz94QQ4N3v3dB4/bKT06RQ1yRF5HtPkqvkGBV6xaG22TdC/TMTUegJIKGf5bsSbdXrGCTunOzD7ssHHTZq+LGR3mlO6d0hztq8EjvSBH0SKF7bM3H7Jr+p4WNciRZudsXNChTXOrUbWic0YNVKugn9If46Z6wUV1naNudQ7ElJve41UqlfNuQWa9J70DmllOPQv+fuvV7YsbHPTnLaPT7rOt/ZPt9k7dza3fkBKg1H41q6W8PzKah/Tup+DP5Recau+/1M9+H/SqffDSvXb7dedap/YtAl0IqiWi29VVrHPs2L4j1qoDYvm/vgCyasknGqNlj6VUFNhXnOBzl3gcKvd8/nFEalSt5C7mNRMFbrqijz1y1xXe30ZqgXHbfS+ku2tJBTrcNHnaHKd18Pi/Z3itsSLH53C3DXze7e2+KivH53DPyysCCCCAAAIIIIAAAgggEE2AQEc0lUxcpsLWH0f84R3xoTsvsyGfDkjqx18TLlGwpHTJ4oEuqTQoudLIPybZytXrnOnDWjaJ24VA7RpVvHzOmZ/aPZG3MBsnpvjG5LjgzB6mFgT7kvyFquqDOjO6lFG3DWr94KY58/9xJ0P1+sSLH3k169Vl2onHHp4m/1NnzveW9eja3gsmeAtzyYQCbaefnNoFzqffjHACZh9/lToIudYnKiDMJZeT6dnITe9xf/Bx+uyUwGxmXnBOPQtHHdHKuwwFOZ5/4zNvfl8n1EWcm/5dllyrKX9hswKrFctHH3/HPW5Wvur8zRrXsT6nHmPPPXyjffXOI1aqZDHvlD+NHOdNuxP586X+mZKeVizu/vvTq7/rOQ06nSjt2JEyLkui7RKt93c9pKCZWvglk5b6xqDyP7vJ7Ms2iQU0Htt5Z3S347q0tduvPc/bYcRvE6zfY697QQpvRZyJOrWqemPAqAtUBTvG/ZUy7oa+L2ONE9egTnXvPTx24nTnDMHxOWjREYedVQgggAACCCCAAAIIIJDJAqklCJl8YA6XIjBqzCSvRq8KdI47qq0zloEGkk30c4KvwPnXMZO8wuhYtv5BxgcNG20q0Pd3W9X75OiDkLvHq1MrNdCxeMly+2N8at/P7jbZ9bpqzXrvVMkOuuvtEGVC3Rf5+wv/7JvhUbYKLtq9J3Ft2Do1q3o7ffbt8HQVLHg75uDEOwMHBQaqv/LCngEnN2srV6UEyzSfGffDPW5WvJ7cvaMVLpwSGFO3OAO/HmYjR6f0Pa4WOD2P75wVpw3FMXPTe1wttdw0fNR4W+17z7vLI1/Vcio9KSeehWOPPMyqV6ngZVOfC24LO29hkhNyWbFyjbd1reqphdsz5ywKjJfjbRQx8d3Q370lKa0A4o/j4m2cDRPVqlSw7ke3987kH4PEXVipYmpgxv855K4/UF7VOvOTr1O732tQr0bUSy9Zsri3XH8DZEZSF1n+AaD9z1Ss42uAbLerLQUda1ZLrRQQax+Wp0+g29HtvB00xo1aTrnpmx9+tSdfTOnO1F2W6LWtr5spdV/lBi4a1athxYuldl/mP47urcaJU9L4XeMnzfQ+y/V5lZ4xXfzHZRoBBBBAAAEEEEAAAQQQyIgAgY6MqKVjH39LjK6d2gS66kh0mK4dW3tdI2kMih+Gj4m7y6GHNDS3lqe6I/rwi6GmAb2VVFBx1OGpNY2jHUj/lB7R9mBnlQY6vfex15OuualCmN/HTo522Awtq+Ir3ErU+mL7jp321+RZCc9z8VkneNuoxUusQI4KUz/7ZoRpoM9E6ezTjvW6jNA/+B98PiTRLt762fMWewMEewuzaWLxv//ZXQ+/ak+99LF3Rg0+emGfVCNvhZlVqZzazVAy4w6MGT/Nv3u2Tmscgx5Hd/DO+fjzH3rdoKlQtbRvQF9vowNkIje9xxXIdYOPavn2wFNvOcHZaLdCYxrdcPczNnHK7GirYy7LiWdBtZ+vuLCnlyeNM3Tr/S/alOnzvGWJJlRA/fjzHzjXvMFXe17BIbdmtT6j3/jg27iH0veAvzXTWb2Ojbt9Zq38Yfgf9p8vQBPvuP6C0Gi1/v1BI40FEC8pKJSom8d4++fmdfrOWrJ0hZNFteA885SuUbNb1dclnL9lZNSN07Hw7J6pz87bH39v+s6Pl157/2tvdfej2zmDVHsLmMgSgSsuONUU8HDTe5/+YK++l3of3OWxXtsd2sxbNeyXsV4g9dAY43O4G7fZG+jQZ9Ir73zlLmZ8Dk+CCQQQQAABBBBAAAEEEMguAQIdWSitbkvcgZF1mh7HpBa+JnNa1aDs2K6Ft6k/aOItjJg47aTUVhvPvf6ZVxhxSo/Opm6JEqW7brzQNAaD0tLlq6zvDQ87g6nH2k8BmME/jbYzLrnH3h04ONZm6V7esF5qbe/X3vvGqyEYeaDR46ZYrwvvtC++/zlyVZr5Xice5dUuVL/1V976hD3y7Hum1i+Tp8819RH/xIsfWs8L7nAKXd0BhdMcyLfg4Cb17AxfgdOAVwbaC29+blu3xq5JqyDDw8+8a30u62fLVyTf37nvtOmeVMGpWjcMGTHGLr3xUTvx7Fu8sVt0MBWgPnbPlV7QJvIEDX21h4f8/GfMwJIKpC+/+TGn24zIY2TnvL+wx18g16fngTkIud8+t7zH1bpGA1W7Sa0Xzr3qAdP4PBpkV4HItz8eZLfc94KdesEdpvUZSTnxLBx/TAc71ddySO+Lc6+635597VNTYDZW0nhMH3z2o/Xoc5O9/9mPUTe75aqznPGWtFKfXa+++3XUlmRqIXPtnU97wer6darbqT3Sjr8T9ST7uHDoz2PthLNvsWde/cR0TbGSviP9rQMOaVo/zaYN6qa2XFAXegpCRyaZ6vvnpHNvM30nhC3t3LHTyXe0FktuQKv/gHe9y7rq4l6mIF60pFYybtJ3mr9FkLs8I68XnnW8N/i5uk27ud/z5h/k3j2m/iZ45Jn3nJr9Wqb3+bWXnO6u5jWLBdSFlbqyctMLb3weaAnkLo/22vbQ1G6m1DpDz56SG8iIto+zvmVKiw5NuwOka9p/vFj7shwBBBBAAAEEEEAAAQQQyEyB/Jl5MI4VFBj685+mmspK6u6nVfOGwQ2SmNN4CMNGjnW2VGG8+uj2jwsReYgTjz3Cnn31E2cgSbeAV2NJ9Doh9iDk/mNooOZrL+ntBAC0fNqsBXb+1Q84LT0UfKhVvZLt2rXbps2ab9NmLrBZ8xZ7tbDVaiSz0rmnd7Phv45zauKrgP74s2421QpV1ys7d+50akdPnjHPVqZjYHH1D9/vlovt1vtSCmjk89EXQ52faPlu3riuEwCJts6/TF4///aXE0jQ/VbB49eDR1nHdoeYatBXrHCQLVi81Om+Rl3YKICUVUl9op920f+8wyugo+5D9BorHdO5jT14x2Ved0/RtlMLiTc++M4pNFNB1kXXPWRHHt7KGtevZUWKFLIZsxc6Vov+We4VjkQ7TnYtU+CmRbP6gRYAmvd3l5SVeVHwLW+e9HUR9Plb/b0+0rMyb7nlPa5r7HnCkaZu+dyWZzPnLDT9REv6DFXBW7ItBdxj5NSzcOf159u69Ru9AI0KsdUCQy0s6teuZirAVwu8rdu226rV60yF+JOmzkk4iHDTRnVM3wsqxJaHAqvjJs6wTh1aOM+3AgvTZs53PoP8gdSbruyTrWPTKLj65off2SdfD7eWzeubvj8UMNV9XLtug3O93/wwyvs8bNKglp1wTNrxgbp1aWuvvPuV892ne6qWP4OH/+EY6rNn0tTZzveBxhUIa1LeFSAuV7a0aVBotXJRhQMVNk+dMc8WLVnuXZqe59NOPMqbj5w4pXsne2fgYKeSg+6/gocaN0atZTS2hr6zC+TPbxovLD1J+bn64tPs3sffcHbTwPFnXdbPFNRr3KCWE9DQ3wtDf/kz0HpJY0joM4eUPQL6e++hOy6z9es3eUE/VaxQYKxbl9SurqLlRu9N/c2iv1fcpOOptWe8VLdWVafFjttVmbbVfm6XVvH2ZR0CCCCAAAIIIIAAAgggkJkCBDoyUzPiWN8O+dVbon8w9Y9felPH9i2cvpHVBYmSWnWoNmespHFAunZu4xSCudt0aNPc/INxu8tjvarWe/lypZ2uU5b9t9opTFNBpFsYGWu//Pkz73FSofTFZ59oas2hpIL6z7+L3mpDBX8qHErUjYuOo665Pnj5PrvhrgExByRXIOnuGy90rvuSGx9xzh+vuyMVAH348r321Msfe60kVMD02bdpax47B/P9KpBEKxvf5gknFWhRl1iJUoEC+e3ojq2dArM2vtqYsfbTc3X/bX3tqtuedApi1R2PathHq2WvgNel551i/Qe8E+tw2bJcLW38XR2d5et6JaszoAF705uSGRMmvceMtX1ueI8rbwo+alDql9/5yunyxK1B7M+3uoJSfq+6qJddffuTXqCjdKnUsQj820ebzolnQdf21P3X2lsffe9cnzteggqb9Vz6n81oedZnjlqjVKmY2m2cu919t/a1MqVKOF3lyUy1qP01qd3t9FqmdAl74PZLrH3r1G5p/OuzYjpf/tTGovruSvT9UbVyeXu2/w1Rg6358uUztWK59s4BXhBo3MTppp/IpEJ+Faq/9PaXkatCMa/AfbyWm+p67+6bLowbsNL3V59Tu3otglRR4MOILhU7tU9tKZoeGLVSypM3j9NiY8vWbU6BuAJt0ZLu29UX97ILzjw+2mqWZaGAvt+feuBa63vDI06gTN/X/+v/ivO3pNs9a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"}}},{"metadata":{},"cell_type":"markdown","source":"Fetching month, day of the week, day of month and hour from the timestamp column will help the model to catch the seasonality impact without using the timestamp column when training the model.\n\nAdditionally, I will add is_weekend and season (winter, spring, summer and fall) to emphasize seasonality in the model."},{"metadata":{"trusted":true},"cell_type":"code","source":"# add month, day of week, day of month and hour\nweather_train['month'] = weather_train['timestamp'].dt.month.astype(np.int8)\nweather_train['day_of_week'] = weather_train['timestamp'].dt.dayofweek.astype(np.int8)\nweather_train['day_of_month']= weather_train['timestamp'].dt.day.astype(np.int8)\nweather_train['hour'] = weather_train['timestamp'].dt.hour\n\n# add is weekend column\nweather_train['is_weekend'] = weather_train.day_of_week.apply(lambda x: 1 if x>=5 else 0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def convert_season(month):\n    if (month <= 2) | (month == 12):\n        return 0\n    # as winter\n    elif month <= 5:\n        return 1\n    # as spring\n    elif month <= 8:\n        return 2\n    # as summer\n    elif month <= 11:\n        return 3\n    # as fall","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"weather_train['season'] = weather_train.month.apply(convert_season)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### <a id='3-1-2-2'> 3.1.2.2. Impute missing values for weather variables </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"We had a quite deal of missing values in the training dataset, except site_id and time_stamp:\n![image.png](attachment:image.png)\n\nI will impute evey missing data from the daily mean of each site per month. This is the most logical and [proven method](https://www.kaggle.com/aitude/ashrae-kfold-lightgbm-without-leak-1-08) (so far). For that, I am going to use update function of pandas.\n\nI modified the function from [this notebook](https://www.kaggle.com/aitude/ashrae-kfold-lightgbm-without-leak-1-08) for imputation. If daily means per month per site_id is not available then I will fill it with the mean values of the filler object created by goruping site_id, day and month.","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"**Reset Index for Update**"},{"metadata":{"trusted":true},"cell_type":"code","source":"weather_train = weather_train.set_index(\n    ['site_id','day_of_month','month'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Air temperature**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\nair_temperature_filler = pd.DataFrame(weather_train\n                                      .groupby(['site_id','day_of_month','month'])\n                                      ['air_temperature'].mean(),\n                                      columns=[\"air_temperature\"])\nair_temperature_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of air_temperatures to fill\ntemporary_df = pd.DataFrame({'air_temperature' : weather_train.air_temperature})\n\n# update NA air_temperature values\ntemporary_df.update(air_temperature_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"air_temperature\"] = temporary_df[\"air_temperature\"]\n\ndel temporary_df, air_temperature_filler\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Cloud Coverage**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\ncloud_coverage_filler = pd.DataFrame(weather_train\n                                     .groupby(['site_id','day_of_month','month'])\n                                     ['cloud_coverage'].mean(),\n                                     columns = ['cloud_coverage'])\ncloud_coverage_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"There are still NA values, so I will first fill NA values of the filler with the rounded mean value, because cloud_coverage takes discrete values."},{"metadata":{"trusted":true},"cell_type":"code","source":"round(cloud_coverage_filler.cloud_coverage.mean(),0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cloud_coverage_filler.fillna(round(cloud_coverage_filler.cloud_coverage.mean(),0), \n                             inplace=True)\n\n# create dataframe of cloud_coverages to fill\ntemporary_df = pd.DataFrame({'cloud_coverage' : weather_train.cloud_coverage})\n\n# update NA cloud_coverage values\ntemporary_df.update(cloud_coverage_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"cloud_coverage\"] = temporary_df[\"cloud_coverage\"]\n\ndel temporary_df, cloud_coverage_filler\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Dew Temperature**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\ndew_temperature_filler = pd.DataFrame(weather_train\n                                      .groupby(['site_id','day_of_month','month'])\n                                      ['dew_temperature'].mean(),\n                                      columns=[\"dew_temperature\"])\ndew_temperature_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of dew_temperatures to fill\ntemporary_df = pd.DataFrame({'dew_temperature' : weather_train.dew_temperature})\n\n# update NA dew_temperature values\ntemporary_df.update(dew_temperature_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"dew_temperature\"] = temporary_df[\"dew_temperature\"]\n\ndel temporary_df, dew_temperature_filler\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Precip Depth 1 Hour**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\nprecip_depth_filler = pd.DataFrame(weather_train\n                                   .groupby(['site_id','day_of_month','month'])\n                                   ['precip_depth_1_hr'].mean(),\n                                   columns=['precip_depth_1_hr'])\nprecip_depth_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"There are still NA values, so I will first fill NA values of the filler with the rounded mean value (because precip_depth_1_hour takes discrete values), as I did for cloud_coverage."},{"metadata":{"trusted":true},"cell_type":"code","source":"round(precip_depth_filler['precip_depth_1_hr'].mean(),0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"precip_depth_filler.fillna(round(precip_depth_filler['precip_depth_1_hr'].mean(),0)\n                           , inplace=True)\n\n# create dataframe of precip_depth_1_hr to fill\ntemporary_df = pd.DataFrame({'precip_depth_1_hr' : weather_train.precip_depth_1_hr})\n\n# update NA precip_depth_1_hr values\ntemporary_df.update(precip_depth_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"precip_depth_1_hr\"] = temporary_df[\"precip_depth_1_hr\"]\n\ndel precip_depth_filler, temporary_df\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Sea Level Pressure**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\nsea_level_filler = pd.DataFrame(weather_train\n                                .groupby(['site_id','day_of_month','month'])\n                                ['sea_level_pressure'].mean(),\n                                columns=['sea_level_pressure'])\nsea_level_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"There are still NA values, so I will first fill NA values of the filler with the mean value, as I did for cloud_coverage."},{"metadata":{"trusted":true},"cell_type":"code","source":"mean_sea_level_pressure = round(\n    sea_level_filler\n    ['sea_level_pressure']\n    .astype(float)\n    .mean(),2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sea_level_filler.fillna(mean_sea_level_pressure, inplace=True)\n\n# create dataframe of sea_level_pressure to fill\ntemporary_df = pd.DataFrame({'sea_level_pressure' : weather_train.sea_level_pressure})\n\n# update NA sea_level_pressure values\ntemporary_df.update(sea_level_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"sea_level_pressure\"] = temporary_df[\"sea_level_pressure\"]\n\ndel sea_level_filler, temporary_df\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Wind Direction**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\nwind_direction_filler = pd.DataFrame(weather_train\n                                     .groupby(['site_id','day_of_month','month'])\n                                     ['wind_direction'].mean(),\n                                     columns=['wind_direction'])\nwind_direction_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of wind_direction to fill\ntemporary_df = pd.DataFrame({'wind_direction' : weather_train.wind_direction})\n\n# update NA wind_direction values\ntemporary_df.update(wind_direction_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"wind_direction\"] = temporary_df[\"wind_direction\"]\n\ndel temporary_df, wind_direction_filler\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Wind Speed**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of daily means per site id\nwind_speed_filler = pd.DataFrame(weather_train\n                                 .groupby(['site_id','day_of_month','month'])\n                                 ['wind_speed'].mean(),\n                                 columns=['wind_speed'])\nwind_speed_filler.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create dataframe of wind_speed to fill\ntemporary_df = pd.DataFrame({'wind_speed' : weather_train.wind_speed})\n\n# update NA wind_speed values\ntemporary_df.update(wind_speed_filler, overwrite=False)\n\n# update in the weather train dataset\nweather_train[\"wind_speed\"] = temporary_df[\"wind_speed\"]\n\ndel temporary_df, wind_speed_filler\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# check if NA values left\nweather_train.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Reset indexes to transfrom weather dataframe to original form**"},{"metadata":{"trusted":true},"cell_type":"code","source":"weather_train = weather_train.reset_index()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"weather_train.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### <a id='3-1-2-3'>  3.1.2.3. Convert wind direction  </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Recall that, wind_direction values span between 0 and 365 representing compass direction. I will aggreagte wind_direction values under 0, 1, 2 and 3 each for Northeast, Southeast, Southwest and Northwest directions.\n\nAfter converting to categorical data, I am going to drop wind_direction column."},{"metadata":{"trusted":true},"cell_type":"code","source":"def convert_direction(degrees):\n    if degrees <= 90:\n        return 0\n    # as norteast direction\n    elif degrees <= 180:\n        return 1\n    # as southeast direction\n    elif degrees <= 270:\n        return 2\n    # as southwest direction\n    elif degrees <= 360:\n        return 3\n    # as northwest direction","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"weather_train['wind_compass_direction'] = weather_train.wind_direction.apply(convert_direction)\nweather_train.drop(columns=['wind_direction'], inplace=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-1-3'> 3.1.3. Combine numeric features </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"I am going to add the mean of the numeric weather variables."},{"metadata":{"trusted":true},"cell_type":"code","source":"# create list of weather variables\nweather_variables = [\"air_temperature\", \"cloud_coverage\", \"dew_temperature\",\n                    \"precip_depth_1_hr\", \"sea_level_pressure\", \"wind_speed\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i, j in combinations(weather_variables, 2):\n    train[\"mean\" + i + \"_\" + j] = (weather_train[i] + weather_train[j]) / 2","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-1-4'> 3.1.4. Merge dataframes </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{"trusted":true},"cell_type":"code","source":"# merge dataframes on train dataframe\ntrain = train.merge(building, on = \"building_id\", how = \"left\")\ntrain = train.merge(weather_train, on = [\"site_id\", \"timestamp\"], how='left')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del building\ndel weather_train\ndel weather_variables\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Number of unique columns in the train dataset:\", train.shape[1])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We have 36 columns after the feature generation one of them being the target value: `meter_reading`."},{"metadata":{},"cell_type":"markdown","source":"**Lets check again for the null values one more time**"},{"metadata":{"trusted":true},"cell_type":"code","source":"train.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After the merge we still have some null values, due to the fact that train dataset has more observations than the tweather_train dataset. I am going to keep it as is for now, and impute missing values before creating the ML models."},{"metadata":{},"cell_type":"markdown","source":"## <a id='3-2'> 3.2. Feature Selection & Further Imputation </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"There are several methods and algorithms available for feature selection:\n* [removing highly collinear features](https://stackoverflow.com/questions/29294983/how-to-calculate-correlation-between-all-columns-and-remove-highly-correlated-on/43104383#43104383)\n* [principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis)\n* [functions in the feature selection class from from scikit-learn](https://scikit-learn.org/stable/modules/feature_selection.html)\nand so on...\n\nAs a quick and dirty solution and to start with I am going to look at pearson coefficients correlations. This is not the best solution for feature selection as it only considers the correlation between one feature to the target, one at a time, rather than considering the consolidated effect of the features to the target."},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-2-1'> 3.2.1. Pearson coefficients of the features </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"I am going list how closely related each feature to the target. To do that, I am going to create pearson coefficients dataframe.\n\nA Pearson correlation is a number between -1 and 1 that indicates the extent to which two variables are linearly related ([source](https://www.spss-tutorials.com/pearson-correlation-coefficient/)). So as it gets closer to 1 or -1, the correlation gets stronger."},{"metadata":{"trusted":true},"cell_type":"code","source":"correlations_transformed = pd.DataFrame(train.corr())\ncorrelations_transformed = pd.DataFrame(correlations_transformed[\"meter_reading\"]).reset_index()\n\n# format, and display sorted correlations_transformed\ncorrelations_transformed.columns = [\"Feature\", \"Correlation with meter_reading\"]\ncorr_df = (correlations_transformed[correlations_transformed[\"Feature\"] != \"meter_reading\"]\n                .sort_values(by=\"Correlation with meter_reading\", ascending=True))\ncorr_df","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Strongest correlation we get is -0.1 and 0.2 which is not as high as I expected. I am going to try looking at the correlations with the natural logarithm of the meter reading+1 using np.log1p. This might help, as it helped for the visualization case."},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del corr_df, correlations_transformed\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# add log_meter_reading column to the dataframe\ntrain['log_meter_reading'] = np.log1p(train.meter_reading)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"correlations_transformed = pd.DataFrame(train.corr())\ncorrelations_transformed = pd.DataFrame(correlations_transformed[\"log_meter_reading\"]).reset_index()\n\n# format, and display sorted correlations_transformed\ncorrelations_transformed.columns = [\"Feature\", \"Correlation with log_meter_reading\"]\ncorr_df = (correlations_transformed[correlations_transformed[\"Feature\"] != \"log_meter_reading\"]\n                .sort_values(by=\"Correlation with log_meter_reading\", ascending=True))\ncorr_df","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Some Notes on Collinearity:**\n<br> Usually a data scientist don't want highly collinear features going into the model. \n\nI dropped the original feature if I have generated a new one. There are some collinear features left though from the original dataset: site_id & building_id and air_temperature & dew_temperature.\n\nAs both feature sets play a major role determining log_meter_reading values I am going to keep both sets, and add precip_depth_1_hr additionally."},{"metadata":{},"cell_type":"markdown","source":"For the feautre selection, if pearson coeffiecient is greater than the absolute value of 0.004, I will keep that feature, and this method includes meter_reading in the feature set so I replace it and include the original feature precip_depth_1_hr."},{"metadata":{"trusted":true},"cell_type":"code","source":"initial_feature_list = (corr_df[\n    (corr_df[\"Correlation with log_meter_reading\"] >= 0.004) | \n    (corr_df[\"Correlation with log_meter_reading\"] <= -0.004)][\"Feature\"].\n                        to_list())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"#original_feature_set = ['building_age', 'le_primary_use', 'cloud_coverage', \n#                        'is_weekend','wind_speed', 'day_of_week',\n#                        'wind_compass_direction', 'sea_level_pressure', 'air_temperature',\n#                        'day_of_month', 'dew_temperature', 'hour', \n#                        'month', 'meter', 'building_id', \n#                        'site_id', 'square_feet']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-output":true},"cell_type":"code","source":"# we included meter_reading in the initial feature set\n# which is not a feature\n# replace meter_reading with precip_depth_1_hr\nfor n, i in enumerate(initial_feature_list):\n    if i == \"meter_reading\":\n        initial_feature_list[n] = \"precip_depth_1_hr\"\ninitial_feature_list","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del corr_df, correlations_transformed\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = train[initial_feature_list]\ny = train['log_meter_reading']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Pearson coefficient based feature selection leaves us with {} features.\".\n      format(len(X.columns)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del train\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### <a id='3-2-2'> 3.2.2. Imputation of Train Dataset </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Recall that, although I performed imputation on the train datasets individually we still have some NA values in the training dataset. None of the machine learning models can work with NA values, I am going to replace NA values. To replace I am going to use [fillna function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) for simplicity and to avoid data leakage. With this method  propagate last valid observation forward to next valid backfill.\n\nThis isa benefical point because, it prevents future data leaking into the training dataset. Preventing data leakage is important in a way [defined](https://machinelearningmastery.com/data-leakage-machine-learning/) by the machine learning mastery as follows:\n> Data leakage is when information from outside the training dataset is used to create the model. This additional information can allow the model to learn or know something that it otherwise would not know and in turn invalidate the estimated performance of the model being constructed."},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X.fillna(method='ffill', axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## <a id='3-3'> 3.3. Split train set into training and validation set </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Now we are going to split the training set into train and validation set.\n\n**Training set** will be used to for the model to learn and capture the patterns and relate them to the target.\n<br> **Validation set** will be used for to understand how well the model generalizes learnings and captures to the newly introduced datasets. \n\nThe score of the evaluation metric obtained from the validation set can be an indicator of the test error.\n\nI removed the timestamp column but it is still a time series data, meaning it has its own trends and seasonality factors. If this dataset was time independent, scikit-learn's train_test_split can be applied straightforward, but I want to keep my training and validation set sequential during the split.\n\nSo, I am going to use custom approach and split train and validation set as below. This way, I am going to keep first 75% of the columns as training set and last quarter will be validation set."},{"metadata":{"trusted":true},"cell_type":"code","source":"# split train and validation set into 75 and 25 percent sequentially\nX_train = X[:int(3 * X.shape[0] / 4)]\nX_valid = X[int(3 * X.shape[0] / 4):]\n\ny_train = y[:int(3 * y.shape[0] / 4)]\ny_valid = y[int(3 * y.shape[0] / 4):]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# make sure train and validation sets shape align\nprint(\"Shape of the training set is: \", X_train.shape)\nprint(\"Shape of the validation set is: \", X_valid.shape)\nprint(\"Shape of the training labels are: \", y_train.shape)\nprint(\"Shape of the validation labels are: \", y_valid.shape)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## <a id='3-4'> 3.4. Create a Baseline Metric </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Before diving deep into the ML algorithms, I am going to calculate a common sense baseline. A common sense baseline is defined in this [article](https://towardsdatascience.com/first-create-a-common-sense-baseline-e66dbf8a8a47) in simple terms, how a person has a knowledge in that field would solve the problem without using any data science tricks. Alternatively, as explained in this [post](https://machinelearningmastery.com/implement-baseline-machine-learning-algorithms-scratch-python/), it can be a dummy or simple algorithm, consisting of few lines of code, to use as a baseline metric.\n\nBaseline metrics can be [different](https://machinelearningmastery.com/how-to-get-baseline-results-and-why-they-matter/) in regression and classification problems. For a regression problem it can be a central tendency measure as the result for all predictions, such as the mean or the median.\n\nSince this a regression problem and competition's results will be [evaluated](https://www.kaggle.com/c/ashrae-energy-prediction/overview/evaluation) for root mean squared logarithmic error, \n\n![image.png](attachment:image.png)\n\nBaseline metrics are important in a way that, if a ML model cannot beat the simple and intuitive prediction of a person's or an algorithm's guess, the original problem needs reconsideration or training data needs reframing.\n\nI have already applied log1p transformation to the target so throughout the project I am going to use RMSE (root mean squared error) as the single evaluation metric, which is:\n\n![image.png](https://miro.medium.com/max/966/1*lqDsPkfXPGen32Uem1PTNg.png)\n\nI am aware that those metrics are not the same, however I already decreased the variance in the target by applying log1p, and I expect a machine learning model to generate predictions within the same ranges as log1p of target. By measuring it with RMSE and predicting log1p of the target, I can get closer results to measuring with RMSLE and target.","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"def rmse(y_true, y_pred):\n    return np.sqrt(\n        np.mean(\n            np.square(y_true - y_pred)\n        )\n    )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"baseline_guess = np.median(y_train)\nprint('The baseline guess is a score of %0.2f' % baseline_guess)\nprint(\"Baseline Performance on the valid set: RMSE = %0.4f\" % rmse(y_valid, baseline_guess))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Min value of meter_reading is:\", y.min())\nprint(\"Median value of meter_reading is:\", y.median())\nprint(\"Max value of meter_reading is:\", y.max())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"This shows average estimate is 2 points away from the training median in total. Log_meter_reading values are spread between 0 and 17 so RMSE of 2 shows that the average error from a naive method is about 12%. Although this is a pretty high baseline already let's see if any machine learning model can beat that score. 🧐"},{"metadata":{},"cell_type":"markdown","source":"# <a id='4'> 4. Compare Several Machine Learning Models </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"We did a heavy lifting job until here and faced the challenges of dealing with the big data, failed to use some libraries in feature selection due to the runtime errors. So this showed me that I need some robust algorithms for this project. \n\nSo I will try out three models: a regression model, a distance based model and a tree-based algorithm that can handle big datasets.\n\nI am going to run simple linear regression, k nearest neighbors regressor and a light gradient boosting machine. Light gradient boosting machine already proved its robustness for big data in several notebooks in this competition. Here are some exmaple notebooks here: [Notebook1](https://www.kaggle.com/rohanrao/ashrae-half-and-half/output), [Notebook2](https://www.kaggle.com/isaienkov/lightgbm-fe-1-19)."},{"metadata":{},"cell_type":"markdown","source":"**Function to evaluate model's performance on train and validation set with RMSLE**"},{"metadata":{"trusted":true},"cell_type":"code","source":"def fit_evaluate_model(model, X_train, y_train, X_valid, y_valid):\n    model.fit(X_train, y_train)\n    y_predicted = model.predict(X_valid)\n    return sqrt(mean_squared_error(y_valid, y_predicted))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## <a id='4-1'> 4.1. Linear Regression </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n# create model apply fit_evaluate_model\nlinear_regression = LinearRegression()\nlr_rmse = fit_evaluate_model(linear_regression, X_train, y_train, X_valid, y_valid)\nprint(\"RMSE of the linear regression model is:\", lr_rmse)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Linear regression model guessed almost the same as the baseline model. Let's see how light gbm will perform."},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del linear_regression\ndel lr_rmse\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## <a id='4-2'> 4.2. KNeighbors Regressor</a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Distance based models requires scaling prior to the applying the model. So specific to k-nearest regressor I am going to apply min_max_scaler from the scikit-learn library."},{"metadata":{"trusted":true},"cell_type":"code","source":"# %%time\n# create scaler\n# scaler = MinMaxScaler()\n\n# apply min_max_scaler to training set and transform training set\n# X_train_scaled = scaler.fit_transform(X_train, y_train)\n\n# transform validation set\n# X_valid_scaled = scaler.transform(X_valid)\n\n# knn_regressor = KNeighborsRegressor()\n# knn_rmse = fit_evaluate_model(knn_regressor, X_train_scaled, y_train, X_valid_scaled, y_valid)\n# print(\"RMSE of the k nearest neighbors regressor is:\", knn_rmse)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"It took more than 10 mins to run k-nearest neighbors regressor and it did not generated any results, so I am going to skip that algorithm. "},{"metadata":{},"cell_type":"markdown","source":"## <a id='4-3'> 4.3. Light GBM </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n# create model apply fit_evaluate_model\nlgbm_regressor = lgb.LGBMRegressor(random_state=42)\nlgbm_rmse = fit_evaluate_model(lgbm_regressor, X_train, y_train, X_valid, y_valid)\nprint(\"RMSE of the light gbm regressor is:\", lgbm_rmse)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del lgbm_regressor\ndel lgbm_rmse\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We achieved an improvement compared to linear regression. Linear regression yielded 1.98 of RMSE whereas light GBM's RMSE was 1.44. So, I will continue further tuning and adjusting the light gbm to achieve less RMSE.\n\nLinear regression resulted in worse RMSE in less runtimes. Distance based algorithm had the highest run time. Light gbm has the highest run time, but generated the best result so far. This results are a concrete example that supports Rachel Tatman's (@rctatman) summary table of the machine learning algorithm's performances from [PyCon 2019](https://youtu.be/qw5dBdTXLEs?t=1372).\n\nSo let's start some tuning!"},{"metadata":{},"cell_type":"markdown","source":"## <a id='4-4'>  4.4. Setting the initial parameters of light gbm </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"As light GBM outperformed linear regression, let's set some initial parameters for the algorithm. One note is that, when categorical features are specifically specified, light GBM works better. I am going to treat some of the numeric features as categorical features. Here is how more categorical features work better in light gbm implementation:\n\n> LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies Fisher (1958) to find the optimal split over categories as described here. This often performs better than one-hot encoding.\n\nA good summary and how Light GBM works can be found in this [blog post](https://towardsdatascience.com/lightgbm-vs-xgboost-which-algorithm-win-the-race-1ff7dd4917d), which I also benefitted a lot for this project. Let's move to the implementation.\n\nTo decide on the inital parameters, I used the ones in this [notebook](https://www.kaggle.com/rohanrao/ashrae-half-and-half/output)."},{"metadata":{"trusted":true},"cell_type":"code","source":"# create categorical features \ncategorical_features = ['building_id', 'site_id', 'meter',\n                        'primary_use', 'wind_compass_direction',\n                        'day_of_week', 'hour','is_weekend', 'season']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now we have 15 numerical features and 9 categorical features to implement."},{"metadata":{},"cell_type":"markdown","source":"Here is the definition of the initially set parameters from the [documentation](https://lightgbm.readthedocs.io/en/latest/Parameters.html):\n\n**LGBM train Parameters:**\n* `feature_fraction`: randomly selected part of features on each iteration (tree) (default=1) \n* `num_boost_round`: Number of boosting iterations (default=100)\n* `boosting`: traditional Gradient Boosting Decision Tree\n* `num_leaves`: max number of leaves in one tree (default=31), to control tree complexity\n* `learning_rate`: the rate at which the model learning from the train set and achieving the best results (default=0.1), together with num_leaves used to control overfitting and underfitting\n* `reg_lambda`: parameter to apply L2 regularization (default=0)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# tranform training and validation set into lgbm datasets\ntrain_dataset = lgb.Dataset(X_train, label=y_train, \n                            categorical_feature=categorical_features, \n                            free_raw_data=False)\nvalid_dataset = lgb.Dataset(X_valid, label=y_valid, \n                            categorical_feature=categorical_features, \n                            free_raw_data=False)\n\n# to record eval results for plotting\nevals_result = {} \n\n# initial parameters of light gbm algorithm\ninitial_params = {\"objective\": \"regression\",\n                  \"boosting\": \"gbdt\",\n                  \"num_leaves\": 60,\n                  \"learning_rate\": 0.05,\n                  \"feature_fraction\": 0.85,\n                  \"reg_lambda\": 2,\n                  \"metric\": {'rmse'}\n}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Building model with first 3 quarter pieces and evaluating the model on the last quarter:\")\nlgb_model = lgb.train(initial_params, \n                      train_set = train_dataset, \n                      num_boost_round = 1000, \n                      valid_sets=[train_dataset, valid_dataset],\n                      verbose_eval = 100,\n                      early_stopping_rounds = 500,\n                      evals_result=evals_result)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Training and Validation Error of the Model')\nax = lgb.plot_metric(evals_result, metric='rmse')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"In the initial round we started with the 1.31 RMSE of the validation error, and in the final iteration we received 1.19 RMSE for the validation set, 0.12 point decrease, which is a 9 percent improvement overall.\n\nTraining error decreased more rapidly through the iterations from 0.95 to 0.68, 0.27 point decrease. This shows that the model learns faster from the training set and this is a consequence of model is trained with the same set of training set in every iteration.\n\nWe learned one thing from the initial parameter set round and the iterations: \n\n* Both training and validation errors decreased, so if I set the num_boost rounds to a higher number, I would probably achieve less RMSE in the cost of higher runtimes. \n* Gap between training and validation error increased, so this is a sign of we are overfitting, to eliminate the possibility of overfitting I will introduce cross-validation into the model rather than hyperparameter tuning."},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"del lgb_model\ndel train_dataset\ndel valid_dataset\ndel X_train\ndel X_valid\ndel y_train\ndel y_valid\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# <a id='5-2'> 5. Updated on Version 10: Perform Cross Validation on the Best Model </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Cross validation allows the model to iteratively run on the different portions of the training set. \n\nRecall that, I initially manually split train and test split into 4 pieces, allocated first 3 quarters as training set and last quarter as validation set.\n\nNow, I am going to iteratively run the model on the 75% of the training data and see if RMSE will improve.\n\nI am going to use [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html), and split X to 4 pieces and run light GBM iteratively on the each fold. \n\nHere is a quick visual from scikit-learn's website of how Kfold performs splits:\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# add back to train and validation sets\n# X = pd.concat([X_train, \n#              X_valid])\n\n#y = pd.concat([y_train,\n#               y_valid])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# cretae kfold object and empty model and evaluation lists\nkf = KFold(n_splits=4, shuffle=False, random_state=42)\nmodels = []\nevaluations = []\n\n# dynamically split X and y with the k-fold split indexes\nfor train_index,valid_index in kf.split(X):\n    X_train_kf = X.loc[train_index]\n    y_train_kf = y.loc[train_index]\n    \n    X_valid_kf = X.loc[valid_index]\n    y_valid_kf = y.loc[valid_index]\n    \n    d_train = lgb.Dataset(X_train_kf, \n                          label=y_train_kf,\n                          categorical_feature=categorical_features, \n                          free_raw_data=False)\n    \n    d_valid = lgb.Dataset(X_valid_kf, \n                          label=y_valid_kf,\n                          categorical_feature=categorical_features, \n                          free_raw_data=False)\n    evaluation_result = {}\n    \n    model = lgb.train(initial_params, \n                      train_set=d_train, \n                      num_boost_round=1000, \n                      valid_sets=[d_train, d_valid],\n                      verbose_eval=100, \n                      early_stopping_rounds=500,\n                      evals_result=evaluation_result)\n    \n    models.append(model)\n    evaluations.append(evaluation_result)\n    \n    del X_train_kf, y_train_kf, X_valid_kf, y_valid_kf, d_train, d_valid\n    gc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After performing the cross validation the lowest RMSE is 1.06, overall resuting in avearge RMSE of 1.12. So in the last notebook I will perform cross_validation on the model then generate the predictions."},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Training and Validation Error of the First Fold')\nax = lgb.plot_metric(evaluations[0], metric='rmse')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Training and Validation Error of the Second Fold')\nax = lgb.plot_metric(evaluations[1], metric='rmse')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Training and Validation Error of the Third Fold')\nax = lgb.plot_metric(evaluations[2], metric='rmse')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"In the above folds the gap between the training and validation RMSE is less than the first model built without cross validation. So as we intorduced different training sets, model learned better to interpret the newly intorduced datasets."},{"metadata":{},"cell_type":"markdown","source":"# <a id='5-1'> 5. Perform Hyperparameter Tuning on the Best Model </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"Searching and setting the best and optimal set of parameters for a machine learning model can be defined as hyperparameter tuning. In my previous projects I used [random search](https://en.wikipedia.org/wiki/Random_search) and [grid search](http://) algorithms but in this project I am going to try [hyperopt library](https://github.com/hyperopt/hyperopt), a library built for performing the hyperparameter tuning. Here is a good [notebook](https://www.kaggle.com/eikedehling/tune-and-compare-xgb-lightgbm-rf-with-hyperopt) on understanding how hyperopt works.\n\nI will define a search space for the parameters to be fine tuned, the objective -the evaluation metric that I am trying to decrease so far- and run the optimization function and let hyperopt to suggest me the best parameters.\n\nThe features that I am going to tune is:\n\n* Num_leaves: since this is discrete integer values, `hp.choice` will be used and sequential space of 60, 62, .., 128, 130 will be searched.\n* col_sample_by_tree is the equivalent parameter of the feature_fraction in the previously trained model.\n* Learning rate & (col_sample_by_tree): since they are continuous float values, `hp.uniform` will be used:\n  * Real numbers between 0.01 to 1 will be seacrhed\n  * Real numbers between 0.8 to 1.0 will be searched respectively.\n* Reg_lambda kept constant for this search.\n* Default value for boosting_type is 'gdbt', so I am not going to specify it explicitly.\n\n\nAdjudting num_leaves and learning rate together helps to control the trade-off between underfit and overfit. [Notebook](https://www.kaggle.com/eikedehling/tune-and-compare-xgb-lightgbm-rf-with-hyperopt) contains a handy function already for a light gbm model, I am going to implement and modify it according to my objectives."},{"metadata":{"trusted":true},"cell_type":"code","source":"'''# objective function to optimize\ndef objective(params):\n    # parameters to perform search\n    params = {\n        'num_leaves': int(params['num_leaves']),\n        'colsample_bytree': '{:.2f}'.format(params['colsample_bytree']),\n        'learning_rate': '{:.3f}'.format(params['learning_rate'])}\n    \n    # model and parameters to kept constant\n    lgb_model = lgb.LGBMRegressor(\n        reg_lambda= 2,\n        **params,\n        random_state=42)\n    \n    score = fit_evaluate_model(lgb_model, X_train, y_train, X_valid, y_valid)\n    print(\"RMSE is {:.4f} with parameters {}\".format(score, params))\n    return score\n\n# define search space\nspace = {\n    'num_leaves': hp.choice('num_leaves', range(60, 130, 2)),\n    'colsample_bytree': hp.uniform('colsample_bytree', 0.8, 1.0),\n    'learning_rate': hp.uniform('learning_rate', 0.01, 0.1)\n}\n\nSEED = 42\n# best model with the hyperopt\nbest = fmin(fn=objective,\n            space=space,\n            algo=tpe.suggest,\n            max_evals=5,\n            rstate= np.random.RandomState(SEED))'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"So the best iteration we get is with the parameters:\n\n* num_leaves: 106\n* colsample_bytree: 0.99\n* learning_rate: 0.075\nLet's update our initial parameters, and see if we can lower training and validation RMSE.\n\nSurprisingly, the hyperparameter search's parameters did not result in lower RMSE both for training and validation set. So maybe we already found the optimal model with the selected features and initial parameters.\n\nSo I am going to use the inital parameters specified in the 4.5. Setting the initial parameters of light gbm while generating the predictions."},{"metadata":{},"cell_type":"markdown","source":"# <a id='6'> Conclusions </a>\n<a href='#top'> Back to Top </a>"},{"metadata":{},"cell_type":"markdown","source":"This is the end of the second notebook, if you are still with me, thank you for your patience 😇\n\nIn this notebook I covered:\n\n3. Feature Engineering & Selection\n4. Compare Several Machine Learning Models\n5. Perform Hyperparameter Tuning on the Best Model\n\nusing the findings of the [first notebook](https://www.kaggle.com/cereniyim/save-the-energy-for-the-future-1-detailed-eda/notebook#--Conclusions-).\n\nWith the feature engineering we generated useful features with intuition and combination of numeric features.\n\nLight GBM proved its robustness when working with a large dataset, outperformed linear regression and selected as best model. Thanks to the public notebooks available I was able to start from a good place and lower the RMSE from 1.45 to 1.2 with the initial parameters set. Thank you all Kagglers, for the collaboration!\n\nI also used this as a chance to explore light GBM library. After tuning the parameters of light GBM, I used hyperopt library to perform hyperparameter tuning. It can be an alternative to Grid Search as it performs more comprehensive search. When the model is introduced with cross validation, average validation error is decreased to 1.1, so in the next notebook model will be cross-validated then the predictions will be generated.\n\nBefore moving to the third notebook, I faced the challenges of working with 20 million rows all the time. I received runtime errors and ran out of RAM. Morever, some functions did not work (E.g. Simple Imputer) or a distance based model simply ran forever, I had to skip revisiting feature selection.\n\nKeeping there are 40 million rows in the test dataset, we are ready to generate predictions for our supervised regression model. Based on the 30 features and available, training dataset (1 year time-series data) and our light gbm model let's go and find the energy consumption of the buildings for 2017 and 2018.\n\nI will save the features and taget and move on to the next steps, see you on the [third notebook](https://www.kaggle.com/cereniyim/save-the-energy-for-the-future-3-predictions) 👋"},{"metadata":{"trusted":true},"cell_type":"code","source":"X.to_csv('X.csv', index=False)\ny.to_csv('y.csv', index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}