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advcl_NN_deepsrc.py
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import pandas as pd
from rich import pretty, print
from rich.progress import BarColumn, Progress
from sklearn.metrics import accuracy_score, f1_score
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import LabelEncoder
import utils
def execute_and_report(learn_rate, acti, current_params):
clf = MLPClassifier(
activation=acti,
learning_rate_init=learn_rate,
random_state=5213890,
**current_params,
)
clf.fit(train_x, train_y)
# Apply on the test set and evaluate the performance
y_pred = clf.predict(test_x)
acc = accuracy_score(test_y, y_pred) * 100
f1 = f1_score(test_y, y_pred, average="weighted") * 100
if acc + f1 > 173:
return {
"Params": f"{acti}, {learn_rate}, {current_params['hidden_layer_sizes']}",
"accuracy %": round(acc, 2),
"F1 weighted %": round(f1, 2),
}
pretty.install()
pd.set_option("display.max_rows", None)
# DATASET
train_x, train_y, test_x, test_y = utils.load_tracks_xyz(
buckets="discrete", extractclass=("album", "type"), splits=2
).values()
# feature to reshape
label_encoders = dict()
column2encode = [
("track", "language_code"),
("album", "listens"),
("track", "license"),
("album", "comments"),
("album", "date_created"),
("album", "favorites"),
("artist", "comments"),
("artist", "date_created"),
("artist", "favorites"),
("track", "comments"),
("track", "date_created"),
("track", "duration"),
("track", "favorites"),
("track", "interest"),
("track", "listens"),
]
for col in column2encode:
le = LabelEncoder()
le.fit(test_x[col])
train_x[col] = le.fit_transform(train_x[col])
test_x[col] = le.fit_transform(test_x[col])
label_encoders[col] = le
le = LabelEncoder()
le.fit(train_y)
test_y = le.fit_transform(test_y)
train_y = le.fit_transform(train_y)
class_name = ("album", "type")
# Preparation
count = 0
reports = pd.DataFrame(columns=["Params", "accuracy %", "F1 weighted %"])
params = [
{"hidden_layer_sizes": (350, 350, 350)},
{"hidden_layer_sizes": (350, 350)},
{"hidden_layer_sizes": (350, 250, 150, 100, 50, 20)},
{"hidden_layer_sizes": (350, 200, 100, 50, 20)},
{"hidden_layer_sizes": (350, 200, 20)},
{"hidden_layer_sizes": (350, 100, 20)},
{"hidden_layer_sizes": (350, 20)},
{"hidden_layer_sizes": (40, 40, 40, 40, 40, 40)},
{"hidden_layer_sizes": (40, 40, 40, 40, 40)},
{"hidden_layer_sizes": (40, 40, 40, 40)},
{"hidden_layer_sizes": (40, 40, 40)},
{"hidden_layer_sizes": (40, 40)},
{"hidden_layer_sizes": (40, 33, 27, 20, 13, 8)},
{"hidden_layer_sizes": (40, 30, 20, 10, 5)},
{"hidden_layer_sizes": (40, 30, 20, 10)},
{"hidden_layer_sizes": (40, 20, 10)},
{"hidden_layer_sizes": (40, 20, 8)},
{"hidden_layer_sizes": (40, 20)},
]
testing_params = [{"hidden_layer_sizes": (10,)}]
activations = ["identity", "relu"]
learning_rate_inits = [0.01, 0.001, 0.02]
# progress reporting init
progress = Progress(
"[progress.description]{task.description}",
BarColumn(),
"[progress.percentage]{task.percentage:>3.0f}%",
"{task.completed} of {task.total}",
)
# grid search
with progress:
task_layers = progress.add_task("[red]Hidden layer sizes…", total=len(params))
task_learn = progress.add_task("[green]Learn rate…", total=len(learning_rate_inits))
task_acti = progress.add_task("[cyan]Activation funcs…", total=len(activations))
for current_params in params:
progress.update(task_learn, completed=0)
for learn_rate in learning_rate_inits:
progress.update(task_acti, completed=0)
for acti in activations:
row = execute_and_report(learn_rate, acti, current_params)
if row:
reports = reports.append(row, ignore_index=True)
count += 1
progress.advance(task_acti)
progress.advance(task_learn)
progress.advance(task_layers)
# results
print(reports.sort_values(by=["accuracy %", "F1 weighted %"], ascending=False))
print(f"I have built {count} neural networks")