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creating rough framework for sap model
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Khalim Conn-Kowlessar
parent
c08f74cefb
commit
f941a3c512
2 changed files with 170 additions and 134 deletions
170
model_data/analysis/SapModel.py
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170
model_data/analysis/SapModel.py
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import numpy as np
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import pandas as pd
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import statsmodels.api as sm
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import matplotlib.pyplot as plt
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import pickle
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, \
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median_absolute_error, mean_absolute_percentage_error
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with open("all_data.pkl", "wb") as f:
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all_data = pickle.load(f)
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class SalModel:
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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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# 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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"property-type",
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"built-form",
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# "construction-age-band",
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"number-habitable-rooms",
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"constituency",
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"number-heated-rooms",
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]
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COMPONENT_FEATURES = [
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"walls-description",
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"floor-description",
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"lighting-description",
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"windows-description",
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"roof-description",
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"mainheat-description",
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"main-fuel"
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]
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CATEGORICAL_COLS = [
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"property-type", "built-form", "number-habitable-rooms", "constituency", "number-heated-rooms",
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"lighting-description", "windows-description", "roof-description", "mainheat-description", "main-fuel",
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]
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def __init__(self, data, cleaner):
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self.df = pd.DataFrame(data)
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self.cleaner = cleaner
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self.train_x = None
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self.train_y = None
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self.results = None
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self.model_data = None
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self.fit_error = None
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self.worst = {"errors": pd.DataFrame(), "x": pd.DataFrame()}
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def create_dataset(self):
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model_data = self.df[[self.RESPONSE] + self.COMPONENT_FEATURES + self.BASE_FEATURES]
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model_data = model_data.reset_index(drop=True)
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model_data["idx"] = model_data.index.copy()
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# Append on u-value estimates
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model_data = model_data.merge(
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pd.DataFrame(self.cleaner.cleaned["walls-description"])[
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["original_description", "thermal_transmittance"]].rename(
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columns={"thermal_transmittance": "walls_u_value", }
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),
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how="left",
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left_on="walls-description",
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right_on="original_description"
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) \
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.drop(columns=["original_description"]) \
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.merge(
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pd.DataFrame(self.cleaner.cleaned["floor-description"])[
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["original_description", "thermal_transmittance"]].rename(
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columns={"thermal_transmittance": "floor_u_value", }
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),
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how="left",
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left_on="floor-description",
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right_on="original_description"
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)
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# Take just entries with U-values
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model_data = model_data[
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~pd.isnull(model_data["walls_u_value"]) &
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~pd.isnull(model_data["floor_u_value"])
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]
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model_data = model_data[
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self.BASE_FEATURES + [c for c in self.COMPONENT_FEATURES if c not in [
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"walls-description", "floor-description"]] + ["walls_u_value", "floor_u_value", self.RESPONSE]
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]
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for col in self.CATEGORICAL_COLS:
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model_data[col] = model_data[col].astype('category')
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def make_training_test(self):
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# Split into training and test
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# Dummy data
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pass
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def fit_model(self):
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# Add a constant to the independent value
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x1 = sm.add_constant(self.X)
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# make regression model
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model = sm.OLS(self.Y, x1)
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# fit model and print results
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self.results = model.fit()
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self.fit_error, self.worst["errors"] = self.calculate_regression_metrics(
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y_true=self.Y, y_pred=self.results.fittedvalues
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)
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self.model_data['fit'] = self.results.fittedvalues
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# The worst errors over index heavily for flats
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self.worst["x"] = self.model_data[self.model_data.index.isin(self.worst["errors"].index)]
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@staticmethod
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def plot_regression(df):
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# Extract the "fit" and "actual" columns from the dataframe
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fit = df['fit']
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actual = df['actual']
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# Create an array of x-values (assumed to be sequential integers)
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x = np.arange(len(df))
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# Plot the fit and actual data
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plt.plot(x, fit, color='red', label='Fit')
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plt.plot(x, actual, color='blue', label='Actual')
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# Set labels and title
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plt.xlabel('Index')
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plt.ylabel('Value')
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plt.title('Linear Regression - Fit vs Actual')
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# Display legend
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plt.legend()
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# Show the plot
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plt.show()
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@staticmethod
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def calculate_regression_metrics(y_true, y_pred, n=20):
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"""
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Calculate the 5 most important accuracy metrics for regression.
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Args:
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y_true (array-like): Array of true target values.
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y_pred (array-like): Array of predicted target values.
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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['Mean True Value'] = y_true.mean()
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metrics['Mean Predicted Value'] = y_pred.mean()
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errors = pd.DataFrame()
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errors['Fit'] = y_true
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errors['Actual'] = y_pred
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errors['Residual'] = errors['Actual'] - errors['Fit']
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errors['Absolute Residual'] = np.abs(errors['Residual'])
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worst_errors = errors.nlargest(n, 'Absolute Residual')
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return metrics, worst_errors
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@ -239,71 +239,9 @@ def handler():
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pd.set_option('display.width', 1000)
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df = pd.DataFrame(data)
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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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# 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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"property-type",
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"built-form",
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# "construction-age-band",
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"number-habitable-rooms",
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"constituency",
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"number-heated-rooms",
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]
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component_features = [
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"walls-description",
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"floor-description",
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"lighting-description",
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"windows-description",
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"roof-description",
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"mainheat-description",
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"main-fuel"
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]
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model_data = df[[response] + component_features + base_features]
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model_data = model_data.reset_index(drop=True)
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model_data["idx"] = model_data.index.copy()
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# Append on u-value estimates
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model_data = model_data.merge(
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pd.DataFrame(cleaner.cleaned["walls-description"])[["original_description", "thermal_transmittance"]].rename(
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columns={"thermal_transmittance": "walls_u_value", }
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),
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how="left",
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left_on="walls-description",
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right_on="original_description"
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) \
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.drop(columns=["original_description"]) \
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.merge(
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pd.DataFrame(cleaner.cleaned["floor-description"])[["original_description", "thermal_transmittance"]].rename(
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columns={"thermal_transmittance": "floor_u_value", }
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),
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how="left",
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left_on="floor-description",
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right_on="original_description"
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)
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# Take just entries with U-values
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model_data = model_data[
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~pd.isnull(model_data["walls_u_value"]) &
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~pd.isnull(model_data["floor_u_value"])
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]
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model_data = model_data[
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base_features + [c for c in component_features if c not in [
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"walls-description", "floor-description"]] + ["walls_u_value", "floor_u_value", response]
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]
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# We need to split the data into a train and test set for model build
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categorical_cols = [
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"property-type", "built-form", "number-habitable-rooms", "constituency", "number-heated-rooms",
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"lighting-description", "windows-description", "roof-description", "mainheat-description", "main-fuel",
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]
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# If these categorical variables are not of type 'category', convert them
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for col in categorical_cols:
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model_data[col] = model_data[col].astype('category')
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# Dummy out the categorical variables
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training_data = pd.get_dummies(model_data, columns=categorical_cols, drop_first=True)
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@ -316,88 +254,16 @@ def handler():
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if training_data[col].dtype == object:
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training_data[col] = training_data[col].astype(float)
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import statsmodels.api as sm
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# Assuming 'df' is your DataFrame
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X = training_data.drop(columns=response)
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Y = training_data[response]
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# Add a constant to the independent value
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X1 = sm.add_constant(X)
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# make regression model
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model = sm.OLS(Y, X1)
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# fit model and print results
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results = model.fit()
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print(results.summary())
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_regression(df):
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# Extract the "fit" and "actual" columns from the dataframe
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fit = df['fit']
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actual = df['actual']
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# Create an array of x-values (assumed to be sequential integers)
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x = np.arange(len(df))
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# Plot the fit and actual data
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plt.plot(x, fit, color='red', label='Fit')
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plt.plot(x, actual, color='blue', label='Actual')
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# Set labels and title
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plt.xlabel('Index')
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plt.ylabel('Value')
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plt.title('Linear Regression - Fit vs Actual')
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# Display legend
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plt.legend()
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# Show the plot
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plt.show()
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import numpy as np
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, \
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median_absolute_error, mean_absolute_percentage_error
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def calculate_regression_metrics(y_true, y_pred, n=20):
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"""
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Calculate the 5 most important accuracy metrics for regression.
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Args:
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y_true (array-like): Array of true target values.
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y_pred (array-like): Array of predicted target values.
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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['Mean True Value'] = y_true.mean()
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metrics['Mean Predicted Value'] = y_pred.mean()
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errors = pd.DataFrame()
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errors['Fit'] = y_true
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errors['Actual'] = y_pred
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errors['Residual'] = errors['Actual'] - errors['Fit']
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errors['Absolute Residual'] = np.abs(errors['Residual'])
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worst_errors = errors.nlargest(n, 'Absolute Residual')
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return metrics, worst_errors
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fit_error, worst_errors = calculate_regression_metrics(y_true=Y, y_pred=results.fittedvalues)
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model_data['fit'] = results.fittedvalues
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# The worst errors over index heavily for flats
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worst_x = model_data[model_data.index.isin(worst_errors.index)]
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# Notes
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# TODO: We might want to look at adding in the u-value estimates for the properties that do not have them
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