File size: 2,097 Bytes
8643b59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import pandas as pd
import numpy as np
from sklearn.linear_model import Lasso, LassoCV
from AdvancedAnalytics.Regression import linreg
from AdvancedAnalytics.ReplaceImputeEncode import ReplaceImputeEncode, DT
data_map = {
'Log_Cum_Production': [ DT.Interval , (8.0, 15.0) ],
'Log_Proppant_LB': [ DT.Interval , (6.0, 18.0) ],
'Log_Carbonate': [ DT.Interval , (-4.0, 4.0) ],
'Log_Frac_Fluid_GL': [ DT.Interval , (7.0, 18.0) ],
'Log_GrossPerforatedInterval': [ DT.Interval , (4.0, 9.0) ],
'Log_LowerPerforation_xy': [ DT.Interval , (8.0, 10.0) ],
'Log_UpperPerforation_xy': [ DT.Interval , (8.0, 10.0) ],
'Log_TotalDepth': [ DT.Interval , (8.0, 10.0) ],
'N_Stages': [ DT.Interval , (2, 14) ],
'X_Well': [ DT.Interval , (-100.0, -95.0) ],
'Y_Well': [ DT.Interval , (30.0, 35.0) ],
'Operator': [ DT.Nominal , tuple(range(1, 29))],
'County': [ DT.Nominal , tuple(range(1, 15))]
}
target = "Log_Cum_Production" # Identify Target Attribute in Data File
df = pd.read_csv("OilProduction.csv")
rie = ReplaceImputeEncode(data_map=data_map, nominal_encoding='one-hot',
display=True)
encoded_df = rie.fit_transform(df)
# Define target and features
target = "Log_Cum_Production"
y = encoded_df[target]
X = encoded_df.drop(target, axis=1)
feature_names = X.columns.tolist()
# Find optimal alpha using LassoCV
lasso_cv = LassoCV(cv=10, random_state=42, max_iter=10000)
lasso_cv.fit(X, y)
optimal_alpha = lasso_cv.alpha_
print(f"\nOptimal alpha from CV: {optimal_alpha:.6f}")
# Fit final model with optimal alpha
final_lasso = Lasso(alpha=optimal_alpha, max_iter=10000)
final_lasso.fit(X, y)
# Get selected features
selected_indices = np.where(final_lasso.coef_ != 0)[0]
selected_features = [feature_names[i] for i in selected_indices]
selected_coef = final_lasso.coef_[selected_indices]
linreg.display_metrics(final_lasso, X, y)
print(f"\nSelected {len(selected_features)} features: COEF")
for feature, coef in zip(selected_features, selected_coef):
print("{:.<28s}{:8.4f}".format(feature, coef))
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