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https://github.com/Hestia-Homes/Model.git
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reverting to baseline performance which was just filling area with zero
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Khalim Conn-Kowlessar
parent
68e903e492
commit
0b41057db8
1 changed files with 132 additions and 65 deletions
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@ -11,13 +11,16 @@ from sklearn.linear_model import Lasso
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from sklearn.preprocessing import StandardScaler
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import xgboost as xgb
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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from tqdm import tqdm
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with open("all_data.pkl", "rb") as f:
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all_data = pickle.load(f)
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class SapModel:
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# We want to estimate for making improvements on different property components
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RESPONSE = "environment-impact-current"
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RESPONSE = "current-energy-efficiency"
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# We could potentially build models by constituency to avoid having too many
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# features in the model
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BASE_FEATURES = [
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@ -50,6 +53,12 @@ class SapModel:
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# "lighting-description" # Might not need to use this
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"low-energy-lighting",
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"number-open-fireplaces",
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"mainheatcont-description",
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"fixed-lighting-outlets-count",
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"floor-height",
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"floor-level",
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"total-floor-area",
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"extension-count",
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]
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CATEGORICAL_COLS = [
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@ -69,8 +78,17 @@ class SapModel:
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"glazed-type",
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"glazed-area",
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"construction-age-band",
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# Testing
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"lighting-description"
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"lighting-description",
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"mainheatcont-description",
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"floor-level",
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]
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NUMERICAL_COLUMNS = [
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"photo-supply", "multi-glaze-proportion", "low-energy-lighting", "number-open-fireplaces",
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"fixed-lighting-outlets-count",
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"floor-height",
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"total-floor-area",
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"extension-count",
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]
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def __init__(self, data, cleaner, test_size=0.2, random_state=None):
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@ -145,14 +163,82 @@ class SapModel:
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model_data = model_data.drop(columns=["transaction-type"])
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return model_data
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@staticmethod
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def _clean_numericals(model_data):
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def _clean_numericals(self, model_data):
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for col in ["photo-supply", "multi-glaze-proportion", "low-energy-lighting", "number-open-fireplaces"]:
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# Try binning numericals
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def bucket_and_fill(df, column_name, n_bins=10):
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# Check if the column is numerical
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if np.issubdtype(df[column_name].dtype, np.number):
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# Create a new categorical column from numerical one by binning the data
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df[column_name + "_bucket"] = pd.cut(df[column_name], bins=n_bins).astype(str)
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# Replace missing data with "NO_RECORD"
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df[column_name + "_bucket"] = df[column_name + "_bucket"].fillna("NO_RECORD")
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df[column_name + "_bucket"] = np.where(
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df[column_name + "_bucket"] == "nan",
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"NO_RECORD",
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df[column_name + "_bucket"]
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)
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return df
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bucket_variables = []
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remaining_numericals = [x for x in self.NUMERICAL_COLUMNS if x not in bucket_variables]
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for col in bucket_variables:
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model_data[col] = pd.to_numeric(model_data[col], errors='coerce')
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model_data = bucket_and_fill(model_data, col)
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# Replace the data with the binned version
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model_data = model_data.drop(columns=bucket_variables)
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model_data = model_data.rename(
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columns=dict(zip([c + "_bucket" for c in bucket_variables], bucket_variables))
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)
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for col in remaining_numericals:
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model_data[col] = np.where(
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model_data[col] == "", "0", model_data[col]
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).astype(float)
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# Basic fill the rest of the columns with 0
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# grouping_variables = ["constituency", "built-form", "property-type"]
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# secondary_grouping_variables = ["constituency", "property-type"]
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#
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# def append_missings(model_data, col, grouping_variables, secondary=False):
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# if not secondary:
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# numerical_data = model_data[(model_data[col] != "")].copy()
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# else:
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# numerical_data = model_data[~pd.isnull(model_data[col])].copy()
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#
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# numerical_data[col] = numerical_data[col].astype(float)
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# numerical_data = numerical_data.groupby(grouping_variables, observed=True)[col].mean().reset_index()
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# numerical_data = numerical_data.rename(columns={col: f"{col}_avg"})
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# model_data = model_data.merge(numerical_data, how="left", on=grouping_variables)
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#
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# if not secondary:
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# model_data[col] = np.where(model_data[col] == "", model_data[f"{col}_avg"], model_data[col])
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# else:
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# model_data[col] = np.where(pd.isnull(model_data[col]), model_data[f"{col}_avg"], model_data[col])
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#
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# model_data = model_data.drop(columns=[f"{col}_avg"])
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# model_data[col] = model_data[col].astype(float)
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#
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# return model_data
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#
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# for col in numerical_cols:
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# model_data[col] = pd.to_numeric(model_data[col], errors='coerce')
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# model_data[col] = model_data[col].fillna(model_data[col].mean())
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# model_data = append_missings(model_data, col, grouping_variables)
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#
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# if pd.isnull(model_data[col]).sum():
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# model_data = append_missings(model_data, col, secondary_grouping_variables, True)
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#
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# if pd.isnull(model_data[col]).sum():
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# raise Exception("Cleaning failed, missing values remain")
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#
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# model_data[col] = np.where(
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# model_data[col] == "", "0", model_data[col]
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# ).astype(float)
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return model_data
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@staticmethod
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@ -212,7 +298,7 @@ class SapModel:
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features = [
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x for x in self.BASE_FEATURES + self.COMPONENT_FEATURES + [
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"walls_u_value", "floor_u_value", "roof_u_value", self.RESPONSE, "idx"
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"walls_u_value", "floor_u_value", "roof_u_value", self.RESPONSE, "idx", "is_rdsap"
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] if x not in exclude_features
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]
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@ -251,7 +337,9 @@ class SapModel:
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def fit_model(self):
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# Dummy out the categorical variables
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x = pd.get_dummies(self.model_data, columns=self.CATEGORICAL_COLS, drop_first=True)
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binned = []
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x = pd.get_dummies(self.model_data, columns=self.CATEGORICAL_COLS + binned, drop_first=True)
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# Convert booleans to integer
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for col in x.columns:
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@ -276,16 +364,6 @@ class SapModel:
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train_x = train_x.drop(columns=["idx"])
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test_x = test_x.drop(columns=["idx"])
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# importance_df = self.make_importance(train_x)
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# Test dropping the least important features
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# to_drop = importance_df.tail(2)["Feature"].values
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# Dropping this is a good idea
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to_drop = [
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"hotwater-description_Electric immersion, off-peak",
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]
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train_x = train_x.drop(columns=to_drop)
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test_x = test_x[train_x.columns]
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.inspection import permutation_importance
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@ -342,13 +420,13 @@ class SapModel:
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).sort_values("actual", ascending=True).merge(self.model_data[["idx", "property-type"]], on="idx")
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# temp hardcoded values
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best_fit = {'MAPE': 0.042824355225087686, 'Mean Squared Error': 21.49263731368226,
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'Mean Absolute Error': 3.298755911054327, 'R2 Score': 0.794118580154128,
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'Explained Variance Score': 0.794118580154128, 'Median Absolute Error': 2.426789554039914}
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best_fit = {'MAPE': 0.04617542805587113, 'Mean Squared Error': 18.62306128026334,
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'Mean Absolute Error': 2.865262003625814, 'R2 Score': 0.8008316762496143,
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'Explained Variance Score': 0.8008316762496143, 'Median Absolute Error': 1.911197425417548}
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best_predict = {'MAPE': 0.04413439429441669, 'Mean Squared Error': 22.700373062051142,
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'Mean Absolute Error': 3.3961241443022008, 'R2 Score': 0.750296045867001,
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'Explained Variance Score': 0.7503518147827141, 'Median Absolute Error': 2.4442017110145855}
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best_predict = {'MAPE': 0.04358926901734807, 'Mean Squared Error': 21.197491698961528,
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'Mean Absolute Error': 3.046853690257838, 'R2 Score': 0.7215087343364782,
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'Explained Variance Score': 0.7215726927575035, 'Median Absolute Error': 1.921094388694634}
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def check_successes(experiment_error, best_error):
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@ -485,38 +563,7 @@ class SapModel:
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worst_x = worst_x.merge(lasso_worst_fit_errors, left_index=True, right_index=True)
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worst_x = worst_x.sort_values("Absolute Residual", ascending=False)
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def make_importance(self, train_x):
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# Create a DMatrix from your training data
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dtrain = xgb.DMatrix(train_x, label=self.train_y)
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# Set the parameters for the XGBoost model
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params = {
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse'
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}
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# Train the XGBoost model
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model = xgb.train(params, dtrain)
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# Get feature importance scores
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importance_scores = model.get_score(importance_type='gain')
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# Create a dataframe with feature names and importance scores
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importance_df = pd.DataFrame({
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'Feature': importance_scores.keys(),
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'Importance': importance_scores.values()
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})
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# Sort the dataframe by importance score in descending order
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importance_df = importance_df.sort_values(by='Importance', ascending=False)
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# Print the feature importances
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return importance_df
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def detect_multi_collinearity(self):
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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from tqdm import tqdm
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# Get the VIFs for each variable
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vifs = pd.DataFrame()
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vifs["features"] = self.train_x.columns
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@ -616,14 +663,14 @@ class SapModel:
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Returns:
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dict: Dictionary containing the calculated metrics.
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"""
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metrics = {}
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metrics['MAPE'] = mean_absolute_percentage_error(y_true, y_pred)
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metrics['Mean Squared Error'] = mean_squared_error(y_true, y_pred)
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metrics['Mean Absolute Error'] = mean_absolute_error(y_true, y_pred)
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metrics['R2 Score'] = r2_score(y_true, y_pred)
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metrics['Explained Variance Score'] = explained_variance_score(y_true, y_pred)
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metrics['Median Absolute Error'] = median_absolute_error(y_true, y_pred)
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metrics = {
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'MAPE': mean_absolute_percentage_error(y_true, y_pred),
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'Mean Squared Error': mean_squared_error(y_true, y_pred),
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'Mean Absolute Error': mean_absolute_error(y_true, y_pred),
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'R2 Score': r2_score(y_true, y_pred),
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'Explained Variance Score': explained_variance_score(y_true, y_pred),
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'Median Absolute Error': median_absolute_error(y_true, y_pred)
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}
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errors = pd.DataFrame()
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errors['Fit'] = y_true
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@ -645,20 +692,40 @@ from sklearn.ensemble import RandomForestRegressor
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rf = RandomForestRegressor(random_state=self.random_state)
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X = self.df.drop(columns=self.RESPONSE)
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for col in ["photo-supply", "multi-glaze-proportion", "low-energy-lighting", "number-open-fireplaces"]:
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X = X.drop(
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columns=["address", "uprn-source", "heating-cost-potential", "hot-water-cost-potential", "potential-energy-rating",
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"environment-impact-potential", "heating-cost-current", "address3", "local-authority-label",
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"hot-water-cost-current", "county", "postcode", "constituency", "co2-emissions-potential", "address2",
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"posttown", "lighting-cost-potential", "lodgement-datetime", "current-energy-rating", "uprn",
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"energy-consumption-current", "lighting-cost-current", "lodgement-date", "lmk-key",
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"potential-energy-efficiency",
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"energy-consumption-potential", "inspection-date", "co2-emiss-curr-per-floor-area", "address1",
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"constituency-label",
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"building-reference-number", "environment-impact-current", "co2-emissions-current",
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])
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# Note: this probably not the best way to treat low-energy-fixed-light-count, unheated-corridor-length,
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# fixed-lighting-outlets-count
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for col in [
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"photo-supply", "multi-glaze-proportion", "low-energy-lighting", "number-open-fireplaces", "floor-height",
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"low-energy-fixed-light-count", "unheated-corridor-length", "fixed-lighting-outlets-count", "total-floor-area"
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]:
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X[col] = np.where(
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X[col] == "", "0", X[col]
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).astype(float)
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Y = self.df[self.RESPONSE]
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X = pd.get_dummies(X)
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rf.fit(X, Y)
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# Print the name and importance of each feature
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importance_df = []
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for feature, importance in zip(train_x.columns, rf.feature_importances_):
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for feature, importance in zip(X.columns, rf.feature_importances_):
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importance_df.append(
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{
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"Feature": feature,
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"rf_importance": importance
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}
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)
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importance_df = pd.DataFrame(importance_df)
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importance_df = importance_df.sort_values("rf_importance", ascending=False)
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