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Commit d7df2bcb authored by Mohamed Sebabti's avatar Mohamed Sebabti
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model rn

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ml_rn.py
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View file @ d7df2bcb
import numpy as np
import pandas as pd
import time
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import time
from sklearn.neural_network import MLPRegressor
def load_and_describe_data(file_path):
"""
Charge un fichier CSV.
:param file_path: Chemin du fichier CSV
:return: DataFrame Pandas
Charge un fichier CSV et affiche les informations de base.
"""
df = pd.read_csv(file_path)
return df
df = load_and_describe_data('data_sup_0popularity.csv')
print(df.info())
return df
def train_mlp(df):
# 1. Séparation des features et de la cible
start_time = time.time() # ⏳ Timer
# 1️⃣ Séparation des features et de la cible
X = df.drop(columns=["popularity", "id", "artists", "name", "release_date", "date_sortie", "duration_ms", "nom_artiste"])
y = df['popularity']
y = df["popularity"]
# 2. Séparation train/test
# 2️⃣ Split train/test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 3. Normalisation des features
scaler = StandardScaler()
# 3️⃣ Normalisation des features
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# 4. Définition des hyperparamètres à tester
# 4️⃣ Définition des hyperparamètres
param_grid = {
'hidden_layer_sizes': [(50,), (100,), (100, 50), (100, 100)],
'activation': ['relu', 'tanh'],
'solver': ['adam', 'sgd'],
'learning_rate_init': [0.001, 0.01, 0.1],
'max_iter': [500]
"hidden_layer_sizes": [(50,), (100,), (100, 50), (100, 100)],
"activation": ["relu", "tanh"],
"solver": ["adam", "sgd"],
"learning_rate_init": [0.001, 0.01, 0.1],
"max_iter": [500],
"early_stopping": [True] # Arrête si la validation ne s'améliore pas
}
# 5. Recherche des meilleurs hyperparamètres avec GridSearchCV
# 5️⃣ Recherche des meilleurs hyperparamètres
mlp = MLPRegressor(random_state=42)
grid_search = GridSearchCV(mlp, param_grid, cv=3, scoring='r2', verbose=2)
grid_search = GridSearchCV(mlp, param_grid, cv=3, scoring="r2", verbose=2)
grid_search.fit(X_train_scaled, y_train)
# 6. Affichage des meilleurs paramètres
print("Meilleurs paramètres :", grid_search.best_params_)
# 6️⃣ Affichage des meilleurs paramètres
best_params = grid_search.best_params_
print("\n✅ Meilleurs paramètres :", best_params)
# 7. Prédictions avec le meilleur modèle
# 7️⃣ Prédiction avec le meilleur modèle
best_mlp = grid_search.best_estimator_
y_pred = best_mlp.predict(X_test_scaled)
# 8. Évaluation du modèle
# 8️⃣ Évaluation du modèle
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print(f"📊 MLPRegressor Optimisé - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R²: {r2:.3f}")
print(f"\n📊 MLPRegressor - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R²: {r2:.3f}")
# 9️⃣ Ajout des prédictions au DataFrame
df.loc[X_test.index, "pred_mlp"] = y_pred
# 9. Ajout des prédictions au DataFrame original
df.loc[X_test.index, 'pred_mlp'] = y_pred
print(df.head(40))
# ⏳ Temps d'exécution
elapsed_time = time.time() - start_time
print(f"\n⏱️ Temps d'exécution : {elapsed_time:.2f} secondes")
return df
train_mlp(df)
# 📂 Chargement et entraînement
df = load_and_describe_data("data_sup_0popularity.csv")
df = train_mlp(df)
model_rn.txt deleted 100644 → 0
+ 0
− 495
View file @ fea72d79
nohup: ignoring input
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 98159 entries, 0 to 98158
Data columns (total 27 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 98159 non-null object
1 artists 98159 non-null object
2 name 98159 non-null object
3 year 98159 non-null int64
4 acousticness 98159 non-null float64
5 danceability 98159 non-null float64
6 duration_ms 98159 non-null int64
7 energy 98159 non-null float64
8 explicit 98159 non-null int64
9 instrumentalness 98159 non-null float64
10 key 98159 non-null int64
11 liveness 98159 non-null float64
12 loudness 98159 non-null float64
13 mode 98159 non-null int64
14 release_date 98159 non-null object
15 speechiness 98159 non-null float64
16 tempo 98159 non-null float64
17 valence 98159 non-null float64
18 popularity 98159 non-null int64
19 date_sortie 98159 non-null object
20 nom_artiste 98159 non-null object
21 nb_caracteres_sans_espaces 98159 non-null int64
22 nb_artistes 98159 non-null int64
23 featuring 98159 non-null int64
24 duree_minute 98159 non-null float64
25 categorie_annee 98159 non-null int64
26 categorie_tempo 98159 non-null int64
dtypes: float64(10), int64(11), object(6)
memory usage: 20.2+ MB
None
Fitting 3 folds for each of 48 candidates, totalling 144 fits
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.1min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.2min
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.4s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.1s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 16.3s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 21.4s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 18.5s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 23.3s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 7.2s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 5.2s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 11.2s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.8s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 7.5s
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.8s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.4s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.7s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.7min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.8min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.8min
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 46.5s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 48.7s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 33.3s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 47.7s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 37.4s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 30.2s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 10.5s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 9.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 1.1min
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 13.9s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 12.6s
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.1s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.1min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.1min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.6min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.2min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.5min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.4min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.4min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 2.8min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.7min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 4.7s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 18.3s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 3.8s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 17.5s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 24.4s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 23.9s
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.7s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.5min
[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.6s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.7min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.7min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.8min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.3min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.2min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.1min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.1min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.2min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.7min
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 29.3s
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 22.4s
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 34.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.2s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 18.8s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 22.6s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 27.3s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 31.2s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 18.1s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 15.6s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 12.6s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 8.8s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.4s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 14.0s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 55.4s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 55.2s
[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 48.8s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 25.3s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 53.5s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 31.3s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 45.3s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 37.0s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 32.3s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 15.0s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 25.6s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 15.6s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 12.9s
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 17.1s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.8min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.5min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.0min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.2min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.1min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 32.0s
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 17.4s
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 24.2s
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 18.0s
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 20.1s
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 6.8s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.4min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.4min
[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.6s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.1min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.7min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.6min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.4min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.6min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.3min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 46.0s
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 52.7s
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 1.2min
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 13.7s
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.2s
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 23.5s
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning:
21 fits failed out of a total of 144.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
21 fits failed with the following error:
Traceback (most recent call last):
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 866, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/base.py", line 1389, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 754, in fit
return self._fit(X, y, incremental=False)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 496, in _fit
raise ValueError(
ValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.
warnings.warn(some_fits_failed_message, FitFailedWarning)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ 0.54900442 0.55026229 0.55175494 0.54449637 0.54619006 nan
0.55486645 0.55397549 0.5576357 nan 0.54283811 nan
0.5408644 0.50266413 0.49902525 0.17836774 0.54044313 -0.00213426
0.51490498 0.44610184 0.47578625 nan 0.53347605 nan
0.55993497 0.5530103 0.55283927 0.5452355 0.52950521 0.20815397
0.55149549 0.54391725 0.5429399 0.53528469 0.53307444 nan
0.49646465 0.48566926 0.51478239 0.54206741 0.49009549 nan
0.48790796 0.40424641 0.4902751 0.53533079 0.48903065 nan]
warnings.warn(
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
Meilleurs paramètres : {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001, 'max_iter': 500, 'solver': 'adam'}
📊 MLPRegressor Optimisé - MAE: 9.16, RMSE: 12.46, R²: 0.578
id ... pred_mlp
0 7pQSmQ0l7QdBeL9X6CEMbH ... NaN
1 2LcJoQ5SDUZrC2qUjWMEdF ... NaN
2 6RzHyUtRNARYyn2AIuoLnY ... NaN
3 6Kd0I5es8911FZpYhFS053 ... NaN
4 0IhY390qx5QJEnRXpeuEwq ... NaN
5 1e7M98usgS7tK89PoEbqpz ... NaN
6 7mZwaEEaHSeTYC2hTAjUki ... NaN
7 0uzPDHGV0ZAjk8wLlryt7C ... NaN
8 45t91j2BnZBTBtechHEdEo ... 38.985588
9 0CIRYGtFan5C3t1udR9A2p ... NaN
10 4qgBYQNjvnuwxp9a68LG0t ... NaN
11 3PHtZBkUc9dDxnaVH9lpTd ... NaN
12 0PFRTA04YOjj4VT3O6MpgP ... NaN
13 1uRKT2LRANv4baowBWHfDS ... NaN
14 6OgG8LnH5gjxlOmBrogYo5 ... NaN
15 3UT1XINeKNAmRyaCHWBOVt ... NaN
16 1ckIrPqEU43EltNMg8HzlV ... NaN
17 4ZHPfdvTgCDGIeP8fAUVpG ... NaN
18 5U1cEuyrUubzj5gc5T7uND ... NaN
19 0RbSPGMjoqjO0GjPFozPkY ... NaN
20 0Cwl389eg6mvLxcaAbV24Z ... NaN
21 0W18O5YKX34dnMqN8MQFfK ... NaN
22 5hNX9VwbZXJ29NW79dDeDz ... NaN
23 3RDMd9JiKdVik8zjGhZ0wJ ... NaN
24 7MER2sjr6GgSohO2vpNO2g ... NaN
25 7GDciQOihRs8nQFEFOrc5K ... NaN
26 24ejNAm1vGCiaB9GBLos4n ... NaN
27 1oXJHuq86np471uQGQjfqg ... NaN
28 3jfdw2ubsW6gUXrf8MAggA ... NaN
29 0awP7BRdUvnzBCaU9YOpiJ ... NaN
30 3vsv5Rj6zpoIvzl8ciwIpd ... NaN
31 15CxpYrIBCl3PX2sVdgDzw ... NaN
32 6vAyOOwgDTH5LCLY2glRTl ... NaN
33 7JZUD0GPmCKtY5XJWL0dJl ... NaN
34 3Wqq9SNrWfdQWqQg4xeYLo ... NaN
35 3ZgQhe1Sv7mnQjpVTJnOFp ... NaN
36 6IEjcFyfMHwQbe26jLojUj ... NaN
37 5jkjpSsMOfsxgdGScPZVq2 ... NaN
38 42alprYTLPWivGUUbuR4dD ... NaN
39 7C8FT11joTPgBCsoDqG735 ... 30.940608
[40 rows x 28 columns]
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