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Fixes for Presentation

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This commit is contained in:
Giacomo Bertolazzi
2024-08-19 20:58:24 +02:00
parent 142fe5ccdf
commit 8b1c149535
7 changed files with 30 additions and 15 deletions
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12
src/app.py
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@@ -25,7 +25,7 @@ def auto_mpg() -> tuple[Dataset, MLAlgorithm, Any]:
ds.numbers(["HP"])
ds.handle_na()
ds.normalize(excepts=["Cylinders","Year","Origin"])
return (ds, LinearRegression(ds, learning_rate=0.0001), sklearn.linear_model.LinearRegression())
return (ds, LinearRegression(ds, learning_rate=0.0001), sklearn.linear_model.SGDRegressor())
def automobile() -> tuple[Dataset, MLAlgorithm, Any]:
ds = Dataset(REGRESSION + "automobile.csv", "symboling", TargetType.Regression)
@@ -35,12 +35,12 @@ def automobile() -> tuple[Dataset, MLAlgorithm, Any]:
ds.numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
ds.handle_na()
ds.normalize(excepts=attributes_to_modify)
return (ds, LinearRegression(ds, learning_rate=0.004), sklearn.linear_model.LinearRegression())
return (ds, LinearRegression(ds, learning_rate=0.003), sklearn.linear_model.SGDRegressor())
def power_plant() -> tuple[Dataset, MLAlgorithm, Any]:
ds = Dataset(REGRESSION + "power-plant.csv", "energy-output", TargetType.Regression)
ds.normalize()
return (ds, LinearRegression(ds, learning_rate=0.1), sklearn.linear_model.LinearRegression())
ds.normalize(excepts=None)
return (ds, LinearRegression(ds, learning_rate=0.1), sklearn.linear_model.SGDRegressor())
# ********************
# Logistic Regression
@@ -101,7 +101,7 @@ def iris_no_target() -> tuple[Dataset, MLAlgorithm, Any]:
if __name__ == "__main__":
np.set_printoptions(linewidth=np.inf, formatter={'float': '{:>10.5f}'.format})
rand = np.random.randint(0, 4294967295)
#rand = 1997847910 # LiR for power_plant
#rand = 2205910060 # LiR for power_plant
#rand = 347617386 # LoR for electrical_grid
#rand = 1793295160 # MLP for iris
#rand = 2914000170 # MLP for frogs
@@ -110,7 +110,7 @@ if __name__ == "__main__":
np.random.seed(rand)
print(f"Using seed: {rand}")
ds, ml, sk = frogs()
ds, ml, sk = power_plant()
epochs, _, _ = ml.learn(1000, verbose=True)
ml.display_results()