Linear Regression (Regression)

# %%
# Installation

# To install the required package, use the following command:
# !pip install modeva

# %%
# Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='YOUR_LICENSE_KEY')

# %%
# Import required modules
from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoElasticNet

# %%
# Load and prepare dataset
ds = DataSet()
ds.load(name="BikeSharing")
ds.set_random_split()
ds.set_target("cnt")

ds.scale_numerical(features=("cnt",), method="log1p")
ds.preprocess()

# %%
# Execute the preprocessing steps defined above
# --------------------------------------------------------------

# %%
# Train model
# ----------------------------------------------------------
model = MoElasticNet(name="GLM",
                     feature_names=ds.feature_names,
                     feature_types=ds.feature_types,
                     alpha=0.01)
model.fit(ds.train_x, ds.train_y)

# %%
# Basic accuracy analysis
# ----------------------------------------------------------
ts = TestSuite(ds, model)
results = ts.diagnose_accuracy_table()
results.table

# %%
# Coefficient interpretation
# ----------------------------------------------------------
results = ts.interpret_coef(features=("season", "yr", "workingday", "weathersit"))
results.plot()

# %%
# Feature importance
# ----------------------------------------------------------
results = ts.interpret_fi()
results.plot()

# %%
# Main effect plot
# ----------------------------------------------------------
results = ts.interpret_effects(features="hr")
results.plot()

# %%
# Local feature importance analysis
# ----------------------------------------------------------
results = ts.interpret_local_fi(dataset="train",
                                sample_index=15,
                                centered=True)
results.plot()

# %%
# Local feature importance with linear coefficients
# ----------------------------------------------------------
results = ts.interpret_local_linear_fi(dataset="train",
                                       sample_index=15,
                                       centered=True)
results.plot()